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138 papers grouped into meanings · 320 typed relationships (320 verified)
Every paper and every typed relationship, with the evidence behind each. For the readable account, go back to the map. This page is long on purpose: nothing is hidden, so your browser’s find will search every quote on it. To go straight to one paper, jump to a paper.
Ranked by how often later work cites each paper for this term, not by how often it is cited overall. The first list is what the field built on. The second is where the meaning changed, and those papers are usually missing from the first list, because a paper that moves a term gets cited by the field it moved into, not by the one it left.
Introduces subsampling of frequent words during training, yielding significant speedup and more accurate representations for rare words
Introduces two new, computationally efficient log-linear model architectures (CBOW and Skip-gram) for learning word vector embeddings that outperform previous neural network models at much lower computational cost.
Presents a general framework that unifies most existing multi-relational embedding models (e.g., NTN, TransE) under linear and bilinear relation operators
Proposes learning representations by predicting the future in a compact latent embedding space using autoregressive models combined with a contrastive (NCE-based) loss, rather than reconstructing raw high-dimensional inputs
Shows that normalizing embeddings (L2 normalization/cosine similarity) together with an appropriately tuned temperature parameter substantially improves representation quality compared to using un-normalized embeddings.
Learns a linear mapping between a pre-trained word2vec embedding space and the RNN encoder's word embedding space to expand vocabulary to nearly a million words.
MoCo builds a dynamic dictionary with a queue and a moving-averaged (momentum) encoder to produce large and consistent sets of encoded representations (embeddings) for contrastive learning, unlike end-to-end or memory-bank approaches.
Recently, attention has shifted to models that learn graph embeddings with methods inspired by the skip-gram model.
The model parameters can also be seen as latent variables, modeling the non-observed taste of a user and the non-observed properties of an item.
Shows that MLP projection head and stronger data augmentation, originally proposed by SimCLR to improve the embedding/representation space, are orthogonal improvements that also boost the MoCo framework's representation quality.
Each row is a typed relationship between two papers. Click a row to see the evidence.
The same three marks stand beside each paper’s own passage, where they are about that passage alone: a passage marked ● can sit above a relationship marked ○.
“More formally, for each word w∈ D, an internal d_wrd-dimensional feature vector representation is given by the lookup table layer LT_W(·):”
“Each row wu in W can be seen as a feature vector describing a user u and similarly each row hi of H describes an item i.”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datapoint 𝐱 could have been generated.”
“we run the t-SNE algorithm […] to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“One possible option is to consider a global embedding of the graph into a low dimensional space, which is rare in practice for high-dimensional data.”
“Multimodal embeddings relate information from multiple sources such as sound and video […] or images and text.”
“Figure […] shows a two-dimensional embedding of all the ImageNet validation images, colored by a coarse category that they come from. The nice separation testifies to a successful semantic embedding.”
“Conceptual word embeddings […] can be very useful here since related concepts end up being represented by similar vectors.”
“The two word embedding layers embed the one-hot input into a dense word representation. It encodes both the syntactic and semantic meaning of the words. The semantically relevant words can be found by calculating the Euclidean distance between two dense word vectors in embedding layers.”
“We introduce a model for bidirectional retrieval of images and sentences through a multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree relations) into a common space.”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
“We consider learning representations of entities and relations in KBs using the neural-embedding approach.”
“Given a specific input embedding, we derive a prediction by maximizing the compatibility F over SJEas follows: f(x;w) = max_y ∈𝒴 F(x,y; w). The parameter vector w can be written as a D × E matrix W with D being the input embedding dimension and E being the output embedding dimension. This leads to the bi-linear form of the compatibility function: F(x,y;W) = θ(x)^⊤ W φ(y). Here, the input embedding is denoted by θ(x) and the output embedding by φ(y).”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“E∈ℝ^m× K_x is the word embedding matrix.”
“A commonly used approach is to construct a model that provides an embedding or feature representation of the data.”
“We used deep LSTMs with 4 layers, with 1000 cells at each layer and 1000 dimensional word embeddings, with an input vocabulary of 160,000 and an output vocabulary of 80,000.”
“We project the MNIST data set to a 2-dimensional latent space and use this 2D embedding as a visualisation of the data – an embedding for MNIST is shown in figure […]. The classes separate into different regions, suggesting that such embeddings can be useful in understanding the structure of high-dimensional data sets.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“An assumption underlying the embedding process is the following: Assumption maximizing objective […] will result in good embeddings v_w ∀ w∈ V, in the sense that similar words will have similar vectors.”
“To focus specifically on the strengths of these models at producing informative sentence representations, we use sentence embedding as an intermediate step in the NLI classification task: each model must produce a vector representation of each of the two sentences without using any context from the other sentence, and the two resulting vectors are then passed to a neural network classifier which predicts the label for the pair.”
“This could be class labels, a regression score, or an unsupervised cluster id or latent vector (embedding).”
“Formally, an input vector x∈ℛ^d is mapped into a feature space with a mapping function ϕℛ^d→ℛ^k where k≪ d.”
“The decoder network is a function that transform a compact graphics code ( 200 dimensions) to a 150×150 image.”
“In text and language processing, one such problem is learning of an embedding vector for a sentence; that is, to train a model that can automatically transform a sentence to a vector that encodes the semantic meaning of the sentence.”
“In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.”
“Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.”
“We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors.”
“The mean embedding of distribution p in ℋ_k is a unique element μ_k(p) such that 𝐄_𝐱∼ pf( 𝐱) = ⟨f( 𝐱),μ _k( p )⟩ _ℋ_k for all f ∈ℋ_k.”
“Given a large network G=(V,E), the problem of Large-scale Information Network Embedding aims to represent each vertex v∈ V into a low-dimensional space R^d, i.e., learning a function f_G:V→ R^d, where d ≪ |V|. In the space R^d, both the first-order proximity and the second-order proximity between the vertices are preserved.”
“This is generally done by designing a function f which acts as the “image representation extractor”, such that given an image I_i it produces a fixed size vector f(I_i).”
“𝐄∈ℝ^m× K is an embedding matrix.”
“Let 𝒱_w2v denote the word embedding space of these word representations and let 𝒱_rnn denote the RNN word embedding space.”
“In the context of evaluating learned representations of words […] demonstrated that simple arithmetic operations revealed rich linear structure in representation space. One canonical example demonstrated that the vector("King") - vector("Man") + vector("Woman") resulted in a vector whose nearest neighbor was the vector for Queen. We investigated whether similar structure emerges in the Z representation of our generators.”
“This channel provides an embedding for the image. We experiment with two embeddings –”
“While GPS-enabled devices are capable of embedding the precise time, location and orientation of capture in the metadata of a photo, in many instances this information is unavailable or out of date: seconds, hours or sometimes even months.”
“Our network learns a discriminative embedding and a similarity measurement at the same time, thus making full usage of the re-ID annotations.”
“The basic idea is to introduce, for each detection, a real number that serves as a “tag” to identify the group the detection belongs to. In other words, the tags associate each detection with other detections in the same group.”
“Word embeddings represent words as dense unit vectors of real numbers, where vectors that are close together are semantically related.”
“The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks.”
“This is similar in spirit to word2vec […] which learns word representation from natural language sentences by predicting a word given its context.”
“The 𝐚_i or 𝐛_j ∈ℝ^l is an embedding of l-dimensional vector, which can be initialized with some pre-trained word embeddings and organized with parse trees.”
“The logit is generally computed as an inner product z_w = h^Te_w where h is a context vector and e_w is a “word embedding” for w.”
“To obtain a visually-discriminative vector representation of text descriptions, we follow the approach of […] by using deep convolutional and recurrent text encoders that learn a correspondence function with images.”
“However, a Riemannian manifold can be realized as a subset of a Euclidean space (in which case it is said to be embedded in that space) by using the structure of the Euclidean space to induce a Riemannian metric.”
“such as embedding lookup and attention module, remain on the CPU, and all other quantized operations are off-loaded to the TPU.”
“Given the 1-hot encoding of the question words Q={q_1,…, q_T }, we first embed the words to a vector space (learnt end-to-end) to get Q^w = {q^w_1,…, q^w_T }.”
“We extract image embeddings from the final layer below the SoftMax layer (a 2048-dim. vector) and rank images by the Euclidean distance between their embedding vectors.”
“For quantum principal component analysis of classical data (qPCA […]), we choose a data vector v⃗_j at random, and use a quantum random access memory (qRAM […]) to map that vector into a quantum state: v⃗_j → |v_j⟩.”
“Right: t-SNE […] visualization of hidden layer activations of a two-layer GCN trained on the Cora dataset […] using 5% of labels.”
“We fuse features of multiple layers with tensor product and embed them into reproducing kernel Hilbert spaces to match distributions for feature adaptation.”
“We demonstrate the ability of the VGAE and GAE models to learn meaningful latent embeddings on a link prediction task on several popular citation network datastets […].”
“Global conditioning is characterised by a single latent representation vech that influences the output distribution across all timesteps, e.g.a speaker embedding in a TTS model.”
“Many embedding models have proposed to learn vector or matrix representations for entities and relations, obtaining state-of-the-art (SOTA) link prediction results […]. In these embedding models, valid triples obtain lower implausibility scores than invalid triples.”
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
“The aim of social network embedding is to project the social actors into a low-dimensional vector space (a.k.a. embedding space).”
“Popular examples include the text representation known as word2vec […], representations of images by convolutional neural networks […], and image descriptors for instance search […]. Such representations or embeddings are usually real-valued, high-dimensional vectors of 50 to 1000+ dimensions.”
“We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering.”
“first uses an to map each word t to an L-dimension embedding v⃗_t:”
“The basic idea behind node embedding approaches is to use dimensionality reduction techniques to distill the high-dimensional information about a node's graph neighborhood into a dense vector embedding.”
“Examples of domains that are not interpretable are abstract vector spaces (e.g. word embeddings […]), or domains composed of undocumented input features (e.g. sequences with unknown words or symbols).”
“It builds face embeddings based on the triplet loss.”
“The encoder maps each entity v_i ∈𝒱 to a real-valued vector e_i ∈ℝ^d.”
“Formally, the problem of network embedding is ofen formalized as follows: Given an undirected and weighted graph G=(V, E) with V as the node set and E as the edge set, the goal is to learn a function V →R|V |×d that maps each vertex to a d-dimensional (d ≪|V |) latent representation that captures the structural properties of G.”
“Then, after M steps of computation, the set2set model produces a graph level embedding q_t^* which is invariant to the order of the of the tuples T.”
“The function of this descriptor is to produce an embedding of the global distribution of channel-wise feature responses, allowing information from the global receptive field of the network to be used by all its layers.”
“Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.”
“Methodologically, we embed items into a `transition space' where users are modeled as translation vectors operating on item sequences.”
“Such embeddings entail mappings from two (or more) domains into a common vector space in which semantically associated inputs (e.g., text and images) are mapped to similar locations. The embedding space thus represents the underlying domain structure, where location and often direction are semantically meaningful.”
“MF maps users and items into a latent factor space and represents users' preferences and items' features by f-dim latent factor vectors (i.e., p_u∈ℝ^f × 1 and q_i∈ℝ^f × 1).”
“an average pooling layer has been introduced to aggregate frame-level speaker feature vectors to obtain an utterance-level feature vector, i.e., speaker embedding, with a fixed number of dimensions.”
“We use the terms user/item latent factors, memories and embeddings interchangeably.”
“Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence.”
“More formally, network embedding learns an embedding matrix X ∈RD×|V |, with each column corresponding to the representation of a vertex (user) in the network G. In the proposed model, we use a pre-trained embedding layer which maps a user u to her D-dimensional representation xu ∈RD, as shown in Figure 2(b).”
“We develop a data-efficient Graph Convolutional Network (GCN) algorithm RW-GCN, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information.”
“Inspired by […], we project each entity and relation into a unified low-dimensional embedding space. Intuitively, the embedding of a tail entity should be close to its translated head entity embedding.”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
“KGE intends to embed entities and relations in a KG into continuous vector spaces while preserving its inherent structure.”
“GST embeddings can also be viewed as an external memory that stores style information extracted from training data. The reference signal guides memory writes at training time, and memory reads at inference time.”
“a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech without transcripts from thousands of speakers, to generate a fixed-dimensional embedding vector from only seconds of reference speech from a target speaker;”
“we can use a spectral layout to initialize the embedding.”
“The models take as input English strings and produce as output a fixed dimensional embedding representation of the string.”
“In natural language processing (NLP), low-dimensional representations of text – called text embeddings – have been computed with unlabeled data […]. Often the embedding function is trained by using the embedding of a piece of text to predict the surrounding text […].”
“Its learning algorithm is a mix of classical (amortized, variational) expectation maximization but through the reparameterization trick ends up backpropagating through the many layers of the deep neural networks embedded inside of it.”
“In contrast to prior word embeddings […], contextual embeddings, such as BERT […] and ELMo […], can generate different vector representations for the same word in different sentences depending on the surrounding words, which form the context of the target word.”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
“suggest that it is possible to build universal cross-lingual encoders that can encode any sentence into a shared embedding space.”
“However, if more layers are applied, each one will perform a different rotation, and many different rotations together have the capability of enabling a feature map. Data embedded in this feature space can be easily separated into classes employing the regions on the Bloch sphere.”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
“Given this, our goal is to learn two mapping functions: f:ℝ^d_v→ℝ^d and g:ℝ^d_c→ℝ^d that respectively embed video and caption features into a common d-dimensional space, such that the cosine similarity”
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
“The encoder ℰ models the inverse conditional distribution P(𝐳|𝐱), predicting latents 𝐳 given data 𝐱 sampled from the data distribution P_𝐱.”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“Generally speaking, there are two key components in learnable CF models — 1) embedding, which transforms users and items to vectorized representations, and 2) interaction modeling, which reconstructs historical interactions based on the embeddings.”
“Network embedding (or graph embedding) has been widely used in many real-world applications.”
“Specifically, the RotatE model maps the entities and relations to the complex vector space and defines each relation as a rotation from the source entity to the target entity.”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
“Its basic idea is to learn embeddings for entities and relations, satisfying 𝐞_h+𝐫≈𝐞_t if there is a triplet (e_h, e_t, r) in KG.”
“The performance of many supervised computer vision algorithms […] is improved by transfer learning, i.e. by using discriminative embeddings from pretrained networks.”
“For each input element, its embedding feature is the summation of four types of embedding, namely, token embedding, visual feature embedding, segment embedding, and sequence position embedding.”
“and e(x) denotes the embedding of x.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“Recent advances in knowledge-graph-based research focus on knowledge representation learning (KRL) or knowledge graph embedding (KGE) by mapping entities and relations into low-dimensional vectors while capturing their semantic meanings […].”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
“Further methods do not seek to explain in terms of input features, but in terms of the latent space, where the directions in the latent space code for higher-level concepts, such as color, material, object part, or object […].”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
“Based on idea of manifold learning, the student network is learned by feature embedding, which preserves the feature similarities of samples in the intermediate layers of the teacher networks […].”
“The most common paradigm for CF is to learn latent features (a.k.a. embedding) to represent a user and an item, and perform prediction based on the embedding vectors […].”
“unsupervised visual representation learning aims to learn an embedding function f_θ (realized via a DNN) that maps X to V={v_1, v_2,...,v_n} with v_i=f_θ(x_i), such that v_i best describes x_i.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
“(a) Schematic of multiview contrastive representation learning, where an image is split into two views, and passed through two encoders to learn an embedding where the views are close relative to views from other images.”
“where ϕ denotes an embedding function used for dimensionally reduction, ϕ(x)^T is the transpose of ϕ(x) along with the channel dimension, Utri(·) is the operator that extracts the upper triangular elements of a matrix and Vec(·) is vectorization.”
“We call the output of the encoder the 'representations' and the output of the projector the 'embeddings'. The representations are used for downstream tasks and the embeddings are fed to the loss function of Barlow Twins.”
“The training set in the BIER is reweighed via the negative gradient of the loss function to project the input spaces (images) into a collection of independent output spaces. To make BIER more robust, Hierarchical Boosted deep metric learning […] incorporated the hierarchical label information into the embedding ensemble which improves the performance of the model on the large scale image retrieval application.”
“Second, to model the local continuity information, we utilize overlapping patch embedding to tokenize images. As shown in Fig. […](a), we enlarge the patch window, making adjacent windows overlap by half of the area, and pad the feature map with zeros to keep the resolution.”
“We first perform the global average pooling on input features ℱ, leading to a channel descriptor 𝐳∈ℝ^N× 1, which is an embedded global distribution of channel-wise feature responses.”
“In BERTopic, we embed documents to create representations in vector space that can be compared semantically. We assume that documents containing the same topic are semantically similar.”
“In order to do this, we first compute an image embedding using a self-supervised ViT-H/16 network pretrained on ImageNet-22k, and use cosine-similarity as a distance measure between images.”
Images are mapped into a semantic space of words that is learned by a neural network model, so word vectors get implicitly grounded by the visual modality, allowing prototypical instances for various words.
Proposes holographic embeddings (HolE) that use circular correlation of entity embeddings as the compositional operator to create compositional vector space representations of entire knowledge graphs.
Introduces the SGVB estimator and AEVB algorithm that jointly learns a recognition model (encoder) and generative model (decoder) via reparameterization, enabling efficient posterior inference and yielding a latent representation (embedding) usable for coding, denoising, representation, and visualization tasks.
The newer paper builds on word embeddings by generalizing the concept to sentence-level embeddings learned via NLI-trained encoders, extending scope beyond words.— the tool’s reading
“Distributed representations of words (or word embeddings) (Bengio et al., 2003; Collobert et al., 2011; Mikolov et al., 2013; Pennington et al., 2014) have shown to provide useful features for various tasks in natural language processing and computer vision.”
“More formally, for each word w∈ D, an internal d_wrd-dimensional feature vector representation is given by the lookup table layer LT_W(·):”
“Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.”
The dense-vector embedding concept from word representations is carried into a new domain—knowledge graph entities and relations for link prediction.— the tool’s reading
“Recently, convolutional neural networks (CNNs), originally designed for computer vision (LeCun et al., 1998), have significantly received research attention in natural language processing (Collobert et al., 2011; Kim, 2014).”
“research attention in natural language processing (Collobert et al., 2011; Kim, 2014).”
“More formally, for each word w∈ D, an internal d_wrd-dimensional feature vector representation is given by the lookup table layer LT_W(·):”
“Many embedding models have proposed to learn vector or matrix representations for entities and relations, obtaining state-of-the-art (SOTA) link prediction results <cit.>. In these embedding models, valid triples obtain lower implausibility scores than invalid triples.”
The newer paper generalizes word-level lookup embeddings to sentences and documents (Paragraph Vectors) while keeping the same distributed vector representation idea.— the tool’s reading
“…NLP applications such as word representation, named entity recognition, word sense disam-biguation, parsing, tagging and machine translation (Col-lobert & Weston, 2008; Turney & Pantel, 2010; Turian et al., 2010; Collobert et al., 2011; Socher et al., 2011b; Huang et al., 2012; Zou et al., 2013).”
“More formally, for each word w∈ D, an internal d_wrd-dimensional feature vector representation is given by the lookup table layer LT_W(·):”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
The newer paper carries the general concept of embedding into a new mathematical modality—RKHS kernel mean embeddings for distribution matching—rather than word vectors.— the tool’s reading
“More formally, for each word w∈ D, an internal d_wrd-dimensional feature vector representation is given by the lookup table layer LT_W(·):”
“We fuse features of multiple layers with tensor product and embed them into reproducing kernel Hilbert spaces to match distributions for feature adaptation.”
The newer paper carries the latent-vector embedding idea from matrix-factorization recommendation into a translation-based knowledge-graph/sequential transition space, applying it in a new modeling framework rather than merely extending or contesting it.'— the tool’s reading
“The main findings are summarized as follows: BPR-MF and FMC achieve considerably better results than the popularity-based baseline in most cases.”
“FPMC and HRM are essentially combinations of MF and FMC. FPMC beats BPR-MF and FMC mainly on relatively dense datasets like Foursquare and Flixter , and loses on sparse datasets— possibly due to the large number of parameters it introduces.”
“, [18, 19, 21]) make a weaker assumption that users simply prefer observed feedback over unobserved feedback and optimize the pairwise rankings of (positive, non-positive) pairs.”
“Each row wu in W can be seen as a feature vector describing a user u and similarly each row hi of H describes an item i.”
“Methodologically, we embed items into a `transition space' where users are modeled as translation vectors operating on item sequences.”
The latent factor vector idea from matrix factorization recommendation is carried into knowledge-graph entity/relation embeddings for link prediction, a new relational/structural modality.— the tool’s reading
“, BPRMF [34]) recommend items for a user by computing a scalar score based on user and item embeddings, which indicates to which extent the user prefers to the item.”
“• Typical similarity-based methods: we choose the widely used collaborative filtering models, FM [33] and BPRMF [34], because they are the foundations of other baselines and also achieve the state-of-the-art performance on many benchmark datasets.”
“In the early stage of item recommendation, researchers focus on recommending similar users or items to a target user using history interactions alone, such as collaborative filtering (CF) [35], factorization machines [33], matrix factorization techniques [19], BPRMF [34].”
“Each row wu in W can be seen as a feature vector describing a user u and similarly each row hi of H describes an item i.”
“Its basic idea is to learn embeddings for entities and relations, satisfying 𝐞_h+𝐫≈𝐞_t if there is a triplet (e_h, e_t, r) in KG.”
The newer paper carries latent vector representation from matrix factorization into a knowledge-graph context, using translation-based entity/relation embeddings for link prediction rather than pure inner-product recommendation.the concept extends into a new domain.'— the tool’s reading
“ions of the correct items in the •nal list. Baselines. We adopt the following representative and state-of-theart methods as baselines for performance comparison: BPR: „e bayesian personalized ranking [10] model is a popular method for top-N recommendation. We adopt matrix factorization as the prediction component for BPR. BPR HFT: „e hidden factors and topic…”
“Each row wu in W can be seen as a feature vector describing a user u and similarly each row hi of H describes an item i.”
“Inspired by <cit.>, we project each entity and relation into a unified low-dimensional embedding space. Intuitively, the embedding of a tail entity should be close to its translated head entity embedding.”
The probabilistic latent code concept is carried into a contrastive self-supervised representation-learning context, renaming and repurposing it as a deterministic embedding for downstream tasks.assistant{— the tool’s reading
“ntations that resemble simple cells in the brain [48]. The idea was later formalized in the context of probabilistic generative models [27], which have become a staple of modern unsupervised learning [28,22,35,56]. A key problem that generative models must overcome, however, is that not all low-level details are equally important in visual perception: for in…”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
The VAE latent-code idea is carried from generic probabilistic encoding into graph-structured data, producing node embeddings via a GCN-based encoder.— the tool’s reading
“We perform full-batch gradient descent and make use of the reparameterization trick [2] for training.”
“We introduce the variational graph autoencoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE) [2, 3].”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“We demonstrate the ability of the VGAE and GAE models to learn meaningful latent embeddings on a link prediction task on several popular citation network datastets <cit.>.”
Both describe encoder-produced latent vectors for representation; newer paper simply calls the same probabilistic latent code an 'embedding' used for downstream tasks.— the tool’s reading
“Many of these approaches rely either on auto-encoding of images [24, 25, 26] or on adversarial learning [27], jointly modelling data and representation [28, 29, 30, 31].”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
The newer paper builds on the earlier latent code idea, generalizing it into a structured vector space supporting semantic arithmetic within a GAN framework rather than VAE encoding.pdf— the tool’s reading
“A variational sampling approach to generating images (Kingma & Welling, 2013) has had some success, but the samples often suffer from being blurry.”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“In the context of evaluating learned representations of words <cit.> demonstrated that simple arithmetic operations revealed rich linear structure in representation space. One canonical example demonstrated that the vector("King") - vector("Man") + vector("Woman") resulted in a vector whose nearest neighbor was the vector for Queen. We investigated whether s…”
Both describe a learned latent representation of data, but the newer paper reframes it as 'embedding' analogous to word2vec rather than a probabilistic encoder's code.— the tool’s reading
“Image generation Generative models of natural images have enjoyed significant research interest [16, 24, 35].”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“This is similar in spirit to word2vec <cit.> which learns word representation from natural language sentences by predicting a word given its context.”
The newer paper carries the latent code/embedding idea from probabilistic autoencoding into contrastive self-supervised learning, a different training paradigm and objective.— the tool’s reading
“Models based on contrastive losses have significantly outperformed other approaches [64, 30, 43, 53, 16, 41, 15, 19, 62].”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“(a) Schematic of multiview contrastive representation learning, where an image is split into two views, and passed through two encoders to learn an embedding where the views are close relative to views from other images.”
The newer paper builds on the earlier VAE latent code framework, adopting the recognition model but applying embeddings for semi-supervised classification tasks.}— the tool’s reading
“…semi-supervised learning by utilising an explicit model of the data density, building upon recent advances in deep generative models and scalable variational inference, namely auto-encoding variational Bayes and stochastic backpropagation (Kingma and Welling, 2014; Rezende et al., 2014).”
“This optimisation can be done jointly, without resort to the variational EM algorithm, by using deterministic reparameterisations of the expectations in the objective function, combined with Monte Carlo approximation – referred to in previous work as stochastic gradient variational Bayes (SGVB) (Kingma and Welling, 2014) or as stochastic backpropagation (Rez…”
“We exploit the stochastic backpropagation technique described by Kingma and Welling (2014) and Rezende et al. (2014) to allow for efficient optimisation.”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“A commonly used approach is to construct a model that provides an embedding or feature representation of the data.”
The probabilistic latent-code embedding from generative encoders is carried into a new domain—word/image representations for caption generation—rather than merely extended or narrowed.— the tool’s reading
“The first approach to using neural networks for caption generation was proposed by Kiros et al. (2014a) who used a multimodal log-bilinear model that was biased by features from the image. This work was later followed by Kiros et al. (2014b) whose method was designed to explicitly allow for a natural way of doing both ranking and generation.”
“The first approach to using neural networks for caption generation was proposed by Kiros et al. (2014a) who used a multimodal log-bilinear model that was biased by features from the image. This work was later followed by Kiros et al. (2014b) whose method was designed to explicitly allow for a natural way of doing both ranking and generation. Mao et al. (2014…”
“The first approach to using neural networks for caption generation was proposed by Kiros et al. (2014a) who used a multimodal log-bilinear model that was biased by features from the image. This work was later followed by Kiros et al. (2014b) whose method was designed to explicitly allow for a natural way of doing both ranking and generation. Mao et al. (2014…”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“𝐄∈ℝ^m× K is an embedding matrix.”
The newer paper carries the VAE's probabilistic latent-code idea into adversarial (BiGAN/BigBiGAN) training for feature-based classification, a new modeling paradigm and application domain.— the tool’s reading
“VQ-VAEs [33] pair a discrete (vector quantized) encoder with an autoregressive decoder to produce faithful reconstructions with a high compression factor and demonstrate representation learning results in reinforcement learning settings.”
“In the adversarial space, adversarial autoencoders [24] proposed an autoencoder-style encoder-decoder pair trained with pixel-level reconstruction cost, replacing the KL-divergence regularization of the prior used in VAEs [19] with a discriminator.”
“In another proposed VAE-GAN hybrid [21] the pixel-space reconstruction error used in most VAEs is replaced with feature space distance from an intermediate layer of a GAN discriminator.”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“The encoder ℰ models the inverse conditional distribution P(𝐳|𝐱), predicting latents 𝐳 given data 𝐱 sampled from the data distribution P_𝐱.”
The newer paper carries the latent-code embedding idea into a new contrastive/NCE mutual information framework for convolutional feature similarity, rather than directly extending or contesting the VAE formulation.'— the tool’s reading
“For clipping score s to range [ − c, c ] , we applied the non-linearity s (cid:48) = c tanh( ) , which is linear around 0 and saturates towards ± c . that We suspect there may be interesting formal and practical connections between regularization restricts the variance/range/etc of scores that go into the NCE bound, and things like the KL/information cost in…”
“We suspect there may be interesting formal and practical connections between regularization that restricts the variance/range/etc of scores that go into the NCE bound, and things like the KL/information cost in Variational Autoencoders [Kingma and Welling, 2013].”
“…0 and saturates towards ± c . that We suspect there may be interesting formal and practical connections between regularization restricts the variance/range/etc of scores that go into the NCE bound, and things like the KL/information cost in Variational Autoencoders [Kingma and Welling, 2013].”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
Both use encoder-produced latent vectors as representations, but the newer paper drops probabilistic framing, calling it 'embedding' with distance-based semantic similarity rather than a coding distribution.— the tool’s reading
“Representations better suited to such tasks have been learnt using deep neural networks, starting with seminal techniques such as Boltzmann machines [54, 50], autoencoders [23], variational autoencoders [30], generative adversarial networks [20] and autoregressive models [45].”
“This idea shows up in contemporary representation learning in the form of autoencoders [50] and generative models [30, 20], which try to represent a data point or distribution as losslessly as possible.”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
The embedding concept shifts from a visualization/dimensionality-reduction tool (t-SNE) to an architectural component (1x1 convolutions) within deep network design.'}— the tool’s reading
“we run the t-SNE algorithm <cit.> to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
Both use dense vector embeddings, but the newer paper generalizes visualization-oriented embeddings into structured joint image-label compatibility spaces for classification.rn— the tool’s reading
“we run the t-SNE algorithm <cit.> to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“Given a specific input embedding, we derive a prediction by maximizing the compatibility F over SJEas follows: f(x;w) = max_y ∈𝒴 F(x,y; w). The parameter vector w can be written as a D × E matrix W with D being the input embedding dimension and E being the output embedding dimension. This leads to the bi-linear form of the compatibility function: F(x,y…”
Both papers use 'embedding' as a technical term for mapping data into a representation space, but the newer paper repurposes it for RKHS distribution mapping rather than dimensionality reduction visualization.— the tool’s reading
“We start with deep convolutional neural networks (CNN) (Krizhevsky et al., 2012), a strong model when it is adapted to novel tasks (Donahue et al., 2014; Hoffman et al., 2014).”
“Feature Visualization To demonstrate the transferability of the DAN learned features, we follow (Donahue et al., 2014; Tzeng et al., 2014) can make the following interesting observations.”
“We evaluate our method across the 3 transfer tasks, A → W, D → W and W → D, which are commonly adopted in deep learning methods (Donahue et al., 2014; Tzeng et al., 2014).”
“we run the t-SNE algorithm <cit.> to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“The mean embedding of distribution p in ℋ_k is a unique element μ_k(p) such that 𝐄_𝐱∼ pf( 𝐱) = ⟨f( 𝐱),μ _k( p )⟩ _ℋ_k for all f ∈ℋ_k.”
Both use 'embedding' as a dense vector representation, but the newer paper restricts it to a task-specific, end-to-end trained retrieval descriptor rather than a general visualization tool.— the tool’s reading
“we run the t-SNE algorithm <cit.> to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“This is generally done by designing a function f which acts as the “image representation extractor”, such that given an image I_i it produces a fixed size vector f(I_i).”
Both use dense vector 'embeddings' from learned features, but the newer paper narrows the concept to fixed-length ConvNet activations as generic descriptors, dropping the visualization/t-SNE context.— the tool’s reading
“n ILSVRC, as feature extractors on other, smaller, datasets, where training large models from scratch is not feasible due to over-fitting. Recently, there has been a lot of interest in such a use case [1, 5, 17, 25], as it turns out that deep image representations, learnt on ILSVRC, generalise well to other datasets, where they have outperformedhand-craftedre…”
“we run the t-SNE algorithm <cit.> to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
The newer paper carries the embedding concept from visualization-focused dimensionality reduction into a new domain-adaptation context using RKHS kernel mean embeddings for distribution matching.}, while retaining t-SNE for visualization.— the tool’s reading
“we run the t-SNE algorithm <cit.> to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“We fuse features of multiple layers with tensor product and embed them into reproducing kernel Hilbert spaces to match distributions for feature adaptation.”
Both use 'embedding' for learned vector representations, but the newer paper restricts the concept to L2-normalized projection vectors for contrastive loss, a specific specialized use case.— the tool’s reading
“we run the t-SNE algorithm <cit.> to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
Both use embeddings as learned dense projections of features, but the newer paper generalizes this into parameterized similarity-space functions optimized via NCE for representation learning.'— the tool’s reading
“Similarly, large convolutional networks pre-trained on large supervised corpora have been widely used to improve performance across the spectrum of computer vision tasks [Donahue et al., 2014, Ren et al., 2015, He et al., 2017, Carreira and Zisserman, 2017].”
“we run the t-SNE algorithm <cit.> to find a 2-dimensional embedding of the high-dimensional feature space, and plot them as points colored depending on their semantic category in a particular hierarchy.”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
The skip-gram embedding technique for words is carried into a new domain—nodes in attributed multiplex heterogeneous networks—via random walks rather than text sequences.— the tool’s reading
“Following [10, 27, 35], we use random walk to generate node sequences and then perform skip-gram [24, 25] over the node sequences to learn embeddings.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Network embedding (or graph embedding) has been widely used in many real-world applications.”
The newer paper generalizes word-level embeddings to sentence-level fixed vectors using encoders, building on word2vec's dense vector representation concept for broader transfer learning tasks.— the tool’s reading
“of pre-trained word embeddings such as those produced by word2vec (Mikolov et al., 2013) or GloVe (Pennington et al.”
“…the problem by implicitly performing limited transfer learning through the use Figure 1: Sentence similarity scores using embed-dings from the universal sentence encoder. of pre-trained word embeddings such as those produced by word2vec (Mikolov et al., 2013) or GloVe (Pennington et al., 2014).”
“For word level transfer, we use word embeddings from a word2vec skip-gram model trained on a corpus of news data (Mikolov et al., 2013).”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“The models take as input English strings and produce as output a fixed dimensional embedding representation of the string.”
The dense vector embedding concept from word representations is carried into a new domain—knowledge graph entities and relations—for link prediction tasks.a— the tool’s reading
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“We consider learning representations of entities and relations in KBs using the neural-embedding approach.”
The newer paper carries the dense vector embedding idea from purely textual word representations into a multi-modal image-text shared space, extending its application domain.— the tool’s reading
“In language domain, several neural network models have been proposed to learn word/n-gram representations [29, 30, 31, 32, 33, 34], sentence representations [35] and paragraph/document representations [36].”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“We introduce a model for bidirectional retrieval of images and sentences through a multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree re…”
The paper takes the general concept of learned vector embeddings from NLP and applies it to a new domain—grouping visual detections via associative tags—repurposing embedding for object detection/pose grouping.— the tool’s reading
“Works in natural language processing have used vector embeddings to represent the meaning of words, sentences, and paragraphs [30, 24].”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“The basic idea is to introduce, for each detection, a real number that serves as a “tag” to identify the group the detection belongs to. In other words, the tags associate each detection with other detections in the same group.”
The newer paper takes the general notion of dense vector embedding into a new domain—hash-based feature embedding for memory-efficient representation—beyond word vectors.— the tool’s reading
“…et al., 2013; Sermanet et al., 2013; Zeiler & Fergus, 2014), object detection (Girshick et al., 2014; Vinyals et al., 2014), image retrieval (Razavian et al., 2014), speech recognition (Hinton et al., 2012; Graves et al., 2013; Mohamed et al., 2011), and text representation (Mikolov et al., 2013).”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Formally, an input vector x∈ℛ^d is mapped into a feature space with a mapping function ϕℛ^d→ℛ^k where k≪ d.”
The newer paper carries the embedding concept from word-vector NLP into visual encoder representations learned via CPC, citing word2vec as an analogous precedent.》— the tool’s reading
“This result is interesting because it echoes results from natural language processing, where unsupervised features such as word2vec [42] and BERT [13] provide strong performance across many tasks without retraining, which sim-plifies training pipelines and reduces computational requirements.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
The dense embedding concept from word vectors is carried into a new modality—image patches—for dimension reduction in CNNs.— the tool’s reading
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
The newer paper transplants the dense vector embedding idea from word representations into network/graph nodes, applying it to a new domain and modality.— the tool’s reading
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“The aim of social network embedding is to project the social actors into a low-dimensional vector space (a.k.a. embedding space).”
The newer paper carries word-vector embeddings into a new multimodal domain, using them as label representations within an image-label joint compatibility space.— the tool’s reading
“Similarly, DeviSe [16] uses CNN [27] features as input and Word2Vec [35] representations as output embeddings.”
“In Word2Vec [35], a two-layer neural network is trained to predict a set of target words from a set of context words.”
“Finally, distributed text representations [35, 42] learned from large unsupervised text corpora can be employed as structured embeddings.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Given a specific input embedding, we derive a prediction by maximizing the compatibility F over SJEas follows: f(x;w) = max_y ∈𝒴 F(x,y; w). The parameter vector w can be written as a D × E matrix W with D being the input embedding dimension and E being the output embedding dimension. This leads to the bi-linear form of the compatibility function: F(x,y…”
The newer paper generalizes word-level embeddings to sentence-level embeddings, building on the same dense vector representation concept for broader transfer to NLP tasks.— the tool’s reading
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.”
The newer paper carries the embedding concept from word vector representations into a new domain—label/output embeddings for image classification compatibility functions.— the tool’s reading
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors.”
The newer paper generalizes word-level dense embeddings to fixed-size sentence-level embeddings using RNN hidden states, building directly on the earlier distributed representation concept.— the tool’s reading
“Inspired by the word embedding method [4], [5], the authors in [2] proposed an unsupervised learning method to learn a paragraph vector as a distributed representation of sentences and documents, which are then used for sentiment analysis with superior performance.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“In text and language processing, one such problem is learning of an embedding vector for a sentence; that is, to train a model that can automatically transform a sentence to a vector that encodes the semantic meaning of the sentence.”
The dense vector embedding idea from word representations is carried into knowledge graphs, adapting negative sampling for entity/relation embeddings in a new domain and complex space.ed— the tool’s reading
“Negative sampling has been proved quite effective for both learning knowledge graph embedding (Trouillon et al., 2016) and word embedding (Mikolov et al., 2013).”
“Here we use a loss function similar to the negative sampling loss (Mikolov et al., 2013) for effectively optimizing distance-based models: where γ is a fixed margin, σ is the sigmoid function, and We also propose a new approach for drawing negative samples.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Specifically, the RotatE model maps the entities and relations to the complex vector space and defines each relation as a rotation from the source entity to the target entity.”
The newer paper adapts the skip-gram word embedding technique from natural language to a new domain, network graphs, mapping vertices instead of words.— the tool’s reading
“These ideas of using “context” are mostly motivated by a wildly used predictive model, i.e., skip-gram with negative sampling (SGNS) [26].”
“Its very recent advances have been largely influenced by the skip-gram model originally proposed for word embedding [25, 26], whose input is a text corpus composed of sentences in natural language and output is the latent vector representation for each word in the corpus.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Formally, the problem of network embedding is ofen formalized as follows: Given an undirected and weighted graph G=(V, E) with V as the node set and E as the edge set, the goal is to learn a function V →R|V |×d that maps each vertex to a d-dimensional (d ≪|V |) latent representation that captures the structural properties of G.”
The embedding idea moves from word vector semantics into knowledge-graph entities/relations and transition modeling, applying similar translation geometry to a new domain.atel— the tool’s reading
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Methodologically, we embed items into a `transition space' where users are modeled as translation vectors operating on item sequences.”
The vector arithmetic idea from word embeddings is carried into a new modality—generative image models' latent noise space—rather than extended within NLP.— the tool’s reading
“In the context of evaluating learned representations of words (Mikolov et al., 2013) demonstrated that simple arithmetic operations revealed rich linear structure in representation space.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“In the context of evaluating learned representations of words <cit.> demonstrated that simple arithmetic operations revealed rich linear structure in representation space. One canonical example demonstrated that the vector("King") - vector("Man") + vector("Woman") resulted in a vector whose nearest neighbor was the vector for Queen. We investigated whether s…”
The newer paper generalizes word-level embeddings to sentences and documents via Paragraph Vectors, building directly on the earlier word vector representation technique.— the tool’s reading
“Our technique is inspired by the recent work in learning vector representations of words using neural networks (Bengio et al., 2006; Collobert & Weston, 2008; Mnih & Hinton, 2008; Turian et al., 2010; Mikolov et al., 2013a;c).”
“In practice, hierarchical softmax (Morin & Bengio, 2005; Mnih & Hinton, 2008; Mikolov et al., 2013c) is preferred to softmax for fast training.”
“For example, the word vectors can be used to answer analogy questions using simple vector algebra: “King” - “man” + “woman” = “Queen” (Mikolov et al., 2013d).”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
The newer paper repurposes word embeddings as a generic example of abstract vector spaces in a different context (interpretability discussion), moving the concept beyond its original NLP use.}— the tool’s reading
“Examples of domains that are not interpretable are abstract vector spaces (e.g. word embeddings [33]), or domains composed of un-documented input features (e.g. sequences with unknown words or symbols).”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Examples of domains that are not interpretable are abstract vector spaces (e.g. word embeddings <cit.>), or domains composed of undocumented input features (e.g. sequences with unknown words or symbols).”
The core embedding idea—mapping entities to low-dimensional vectors preserving structural relationships—is carried from word/language data into general graph/manifold data representation.dominant shift is domain, not restriction or dispute.— the tool’s reading
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“we can use a spectral layout to initialize the embedding.”
The newer paper transfers the word-embedding concept of learning representations via context prediction into the visual domain, applying it to image patches rather than words.— the tool’s reading
“Interestingly, similar distinction exist in using language context to learn word embeddings: Collobert and Weston [5] advocate a discriminative approach, whereas word2vec [30] formulate it as word prediction.”
“” This is similar in spirit to word2vec [30] which learns word representation from natural language sentences by predicting a word given its context.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“This is similar in spirit to word2vec <cit.> which learns word representation from natural language sentences by predicting a word given its context.”
The newer paper adapts word-embedding techniques (negative sampling, learning rate schedule) from word vectors to a new domain—network vertices—preserving graph proximities instead of linguistic regularities.— the tool’s reading
“To address this problem, we adopt the approach of negative sampling proposed in [13], which samples multiple negative edges according to some noisy distribution for each edge (i, j).”
“Similar to [13], the learning rate is set with the starting value ρ0 = 0.”
“We set Pn(v) ∝ dv 3/4 as proposed in [13], where dv is the out-degree of vertex v.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Given a large network G=(V,E), the problem of Large-scale Information Network Embedding aims to represent each vertex v∈ V into a low-dimensional space R^d, i.e., learning a function f_G:V→ R^d, where d ≪ |V|. In the space R^d, both the first-order proximity and the second-order proximity between the vertices are preserved.”
The newer paper generalizes word2vec's embedding concept into a formal representation function f applicable to text and images within a contrastive learning theory framework.rieben— the tool’s reading
“The use of co-occurring words as semantically similar points and negative sampling for learning word embeddings was introduced in Mikolov et al. (2013).”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“In natural language processing (NLP), low-dimensional representations of text – called text embeddings – have been computed with unlabeled data <cit.>. Often the embedding function is trained by using the embedding of a piece of text to predict the surrounding text <cit.>.”
The Skip-gram embedding idea for words is carried into a new domain—knowledge graph entities/relations—for link prediction tasks.— the tool’s reading
“RDF2Vec extracts walks on labeled graphs which are then processed using the Skipgram (Mikolov et al. 2013) model to generate entity embeddings, used for subsequent classification.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“The encoder maps each entity v_i ∈𝒱 to a real-valued vector e_i ∈ℝ^d.”
The dense embedding concept from word vector learning is carried into the image domain for contrastive visual representation learning, a new modality entirely.— the tool’s reading
“For example: luminance and chrominance decomposition [53], randomly augmenting an image twice [58, 7, 4, 23, 60, 50, 65, 67], using different time-steps of videos [42, 66, 46, 22, 21], patches of the same image [27, 42, 26], multiple sensory data [39, 9, 44], or text and its context [37, 59, 35, 32].”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“(a) Schematic of multiview contrastive representation learning, where an image is split into two views, and passed through two encoders to learn an embedding where the views are close relative to views from other images.”
The dense vector embedding concept from word representations is carried into a new domain—graph nodes—using GCNs instead of word co-occurrence.— the tool’s reading
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“We develop a data-efficient Graph Convolutional Network (GCN) algorithm RW-GCN, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information.”
The embedding-based generative framework from word vectors is carried into a new domain—knowledge graphs and recommendation—applying it to entities and relations rather than words.— the tool’s reading
“bserving et but low probability for observing e′ t, which is exactly the goal of the embedding-based generative framework. The embedding-based generative framework is first proposed by Mikolov et al. [20] and has been widely used in word embedding [14, 20], recommendation [30, 33], and information retrieval tasks [1, 2]. Formally, for an observed relation tr…”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Inspired by <cit.>, we project each entity and relation into a unified low-dimensional embedding space. Intuitively, the embedding of a tail entity should be close to its translated head entity embedding.”
The skip-gram embedding idea from word vectors is carried into graph domain to represent nodes, applying the same technique to a new modality (networks).— the tool’s reading
“Recently, attention has shifted to models that learn graph embeddings with methods inspired by the skip-gram model (Mikolov et al., 2013).”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Right: t-SNE <cit.> visualization of hidden layer activations of a two-layer GCN trained on the Cora dataset <cit.> using 5% of labels.”
The newer paper takes the word-vector embedding concept and applies the same low-dimensional geometric mapping idea to graphs, manifolds, and general data points beyond language.rains— the tool’s reading
“In natural language processing, words in a corpus can be represented by the co-occurrence graph, where two words are connected if they often appear near each other [41].”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“However, a Riemannian manifold can be realized as a subset of a Euclidean space (in which case it is said to be embedded in that space) by using the structure of the Euclidean space to induce a Riemannian metric.”
The dense vector embedding concept from word representations is carried into a new domain—knowledge graph entities/relations and items/users—reusing negative sampling techniques.'— the tool’s reading
“ng iteration, to make the computation more efficient, we randomly sample a minibatch of positive/negative interactions from Y and true/false triples from G following the negative sampling strategy in [16]. Then we calculate the gradients of the loss Lwith respect to model parameters Θ, and update all parameters by back-propagation based on the sampled miniba…”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“KGE intends to embed entities and relations in a KG into continuous vector spaces while preserving its inherent structure.”
The newer paper generalizes word embeddings from standalone Skip-gram vectors to layers learned jointly within a larger neural network, building on the same distributed representation idea.— the tool’s reading
“For natural language, the Recurrent Neural Network (RNN) shows the state-of-the-art performance in many tasks, such as speech recognition and word embedding learning (Mikolov et al. (2010; 2011; 2013)).”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“The two word embedding layers embed the one-hot input into a dense word representation. It encodes both the syntactic and semantic meaning of the words. The semantically relevant words can be found by calculating the Euclidean distance between two dense word vectors in embedding layers.”
The newer paper carries the embedding concept from word-vector NLP representations into contrastive representation learning with convolutional features for mutual information estimation, a new modality/domain.ed— the tool’s reading
“For example, consider the ubiquitous use of pre-trained model components, such as word vectors [Mikolov et al., 2013, Pennington et al., 2014] and context-sensitive encoders [Peters et al., 2018, Devlin et al., 2019], for achieving state-of-the-art results on hard NLP tasks.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
The dense-vector embedding concept from word representations is carried into a new domain—graph nodes—using an inductive neighborhood-aggregation method rather than direct generalization or renaming.— the tool’s reading
“75 , following [11, 22, 28].”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“The basic idea behind node embedding approaches is to use dimensionality reduction techniques to distill the high-dimensional information about a node's graph neighborhood into a dense vector embedding.”
The earlier word-embedding concept from NLP (Skip-gram) is carried into a new modality—visual encoder representations of image views—retaining dense vector similarity semantics.two— the tool’s reading
“to approximate the full softmax, such a trick was also used in [40] for example; (b) contrasting sub-patches rather than full images to increase the number of negatives inside each batch, similar to [38, 59, 24]. 3.5.1 Leveraging Noise-Contrastive Estimation Given an anchor vi 1from V, the probablity that an atom v2 2fv j 2jj= 1;2;:::;Ngfrom V2 is the best m…”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
The newer paper carries the embedding concept from word vectors into image and question representations for VQA, a new modality/domain.— the tool’s reading
“We avoid using soft metrics such as Word2Vec [33], since they often group together words that we wish to distinguish, such as “left” and “right”.”
“Distributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.”
“This channel provides an embedding for the image. We experiment with two embeddings –”
The skip-gram embedding technique for words is carried into a new domain—network nodes—reusing the same learning mechanism for graph representations.'— the tool’s reading
“Following [10, 26, 33], we use random walk to generate node sequences and then perform skip-gram [23, 24] over the node sequences to learn embeddings.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Network embedding (or graph embedding) has been widely used in many real-world applications.”
The newer paper applies the general skip-gram word embedding method to a specific domain task—tag normalization and similarity retrieval—without extending the underlying representation theory.— the tool’s reading
“Conceptual word embeddings [15] can be very useful here since related concepts end up being represented by similar vectors.”
“After pre-processing and cleaning of the text we trained a skip-gram model [15] with word vector size of 200.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Conceptual word embeddings <cit.> can be very useful here since related concepts end up being represented by similar vectors.”
The newer paper builds on word embeddings as input representations and generalizes the idea to fixed-dimensional vectors for entire sequences, not just words.— the tool’s reading
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“We used deep LSTMs with 4 layers, with 1000 cells at each layer and 1000 dimensional word embeddings, with an input vocabulary of 160,000 and an output vocabulary of 80,000.”
The embedding concept moves from word vectors in NLP to image/patch encoder representations in CPC-based visual pre-training, applying dense vector idea to a new modality.— the tool’s reading
“For example, camera motion [1, 32], tracking [61], text proximity [41], and other cues [11, 62, 30, 60, 29] can be used to find examples of features that should be ‘similar’ or predictable from one another, whereas negative examples are usually sampled randomly.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
The newer paper generalizes word embeddings into a multi-modal composite embedding (visual, segment, position) for Transformer input, building on the earlier low-level linguistic embedding concept.— the tool’s reading
“There are previous research works on improving word embedding (Mikolov et al., 2013; Pennington et al., 2014; Kiros et al., 2015), which is a low-level linguistic feature representation.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“For each input element, its embedding feature is the summation of four types of embedding, namely, token embedding, visual feature embedding, segment embedding, and sequence position embedding.”
The embedding concept moves from word vector representations in NLP to dense image feature vectors in convolutional vision architectures, a new modality and field.— the tool’s reading
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
The newer paper generalizes word-level embeddings into a structured, attention-weighted matrix representation for whole sentences, building on the earlier vector-space concept.— the tool’s reading
“Much progress has been made in learning semantically meaningful distributed representations of individual words, also known as word embeddings (Bengio et al., 2001; Mikolov et al., 2013).”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
The newer paper carries the word-embedding concept from purely linguistic vector space into a multimodal text-video joint embedding domain, using word2vec as a component.— the tool’s reading
“For the word representations, we use the GoogleNews pre-trained word2vec embedding model [34].”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Given this, our goal is to learn two mapping functions: f:ℝ^d_v→ℝ^d and g:ℝ^d_c→ℝ^d that respectively embed video and caption features into a common d-dimensional space, such that the cosine similarity”
The newer paper carries the word-embedding concept from linguistic word representation into knowledge-graph entities/relations for link prediction, a new domain and modality of application.“— the tool’s reading
“It has been shown in [87] that MLPs can learn to put “semantically similar” words close by in the embedding space, even if they are not explicitly trained to do so.”
“In particular, the score of a triple x ijk is defined as: This model is inspired by the results in [87], who showed that some relationships between words could be computed by their vector difference in the embedding space.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“This could be class labels, a regression score, or an unsupervised cluster id or latent vector (embedding).”
The newer paper generalizes word-level embeddings into fixed-size sentence-level embeddings using RNNs, building on the earlier distributed vector representation concept.— the tool’s reading
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“In text and language processing, one such problem is learning of an embedding vector for a sentence; that is, to train a model that can automatically transform a sentence to a vector that encodes the semantic meaning of the sentence.”
The newer paper carries word2vec's contrastive prediction embedding idea into a new modality—general high-dimensional signals like images and audio via encoder-based latent representations.— the tool’s reading
“In Word2Vec [9] neighbouring words are predicted using a contrastive loss.”
“Recent work in unsupervised learning has successfully used these ideas to learn word representations by predicting neighboring words [9].”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
The skip-gram word embedding technique from NLP is carried into a new domain—graphs—to represent vertices instead of words in vector space.— the tool’s reading
“Its very recent advances have been largely influenced by the skip-gram model originally proposed for word embedding [25, 26], whose input is a text corpus composed of sentences in natural language and output is the latent vector representation for each word in the corpus.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Formally, the problem of network embedding is ofen formalized as follows: Given an undirected and weighted graph G=(V, E) with V as the node set and E as the edge set, the goal is to learn a function V →R|V |×d that maps each vertex to a d-dimensional (d ≪|V |) latent representation that captures the structural properties of G.”
The newer paper builds on word vector embeddings by generalizing them to Paragraph Vectors, extending fixed-length vector representations from words to sentences and documents.— the tool’s reading
“Our technique is inspired by the recent work in learning vector representations of words using neural networks (Bengio et al., 2006; Collobert & Weston, 2008; Mnih & Hinton, 2008; Turian et al., 2010; Mikolov et al., 2013a;c).”
“A particular implementation of neural network based al-gorithm for training the word vectors is available at code.google.com/p/word2vec/ (Mikolov et al., 2013a).”
“In practice, hierarchical softmax (Morin & Bengio, 2005; Mnih & Hinton, 2008; Mikolov et al., 2013c) is preferred to softmax for fast training.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
Newer paper builds on distributional word vector concept, adding knowledge-graph-derived embeddings and retrofitting to enhance semantic representation beyond pure distributional learning.ed— the tool’s reading
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Word embeddings represent words as dense unit vectors of real numbers, where vectors that are close together are semantically related.”
The newer paper carries the embedding concept from word representations into a new domain—network vertices—defining analogous low-dimensional vector representations for graphs.— the tool’s reading
“Two applications are used to evaluate the effectiveness of the learned embeddings: word analogy [12] and document classification.”
“This shows that the second-order proximity suffers when the network is extremely sparse, and it outperforms first-order proximity when there are sufficient nodes in the neighborhood of a node.”
“This task is introduced by Mikolov et al. [12].”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Given a large network G=(V,E), the problem of Large-scale Information Network Embedding aims to represent each vertex v∈ V into a low-dimensional space R^d, i.e., learning a function f_G:V→ R^d, where d ≪ |V|. In the space R^d, both the first-order proximity and the second-order proximity between the vertices are preserved.”
The newer paper carries word-embedding techniques (negative sampling, noisy distribution) into knowledge graph entity/relation modeling for link prediction, a new domain rather than a generalization or dispute.— the tool’s reading
“rize multiple relation matrix on a product knowledge graph. As shown in Equation (8), the final objective of the proposed model is controlled by the noisy distribution Pt. Similar to previous studies [1, 14, 19], we notice that the relationships with tail entities that have high frequency in the collection reveal less information about the properties of the …”
“(2). The computation cost of L(S), however, is prohibitive in practice because of the softmax function. For efficient training, we adopt a negative sampling strategy to approximate P(et |trans(eh,r))[19]. Specifically, for each observed relation triplet( e ,t r), we randomly sample a set of “negative” entities with the same type of e . Then the log likelihoo…”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Inspired by <cit.>, we project each entity and relation into a unified low-dimensional embedding space. Intuitively, the embedding of a tail entity should be close to its translated head entity embedding.”
The newer paper takes word embeddings from purely linguistic space and extends the concept into a joint multimodal image-text embedding, applying it to a new modality.— the tool’s reading
“Word embeddings learned with skip-gram [37] or neural language models [45] were shown by [12] to exhibit linguistic regularities that allow these models to perform analogical reasoning.”
“For all of our experiments, we initialize the word embeddings WT to be pre-computed K = 300 dimensional vectors learned using a continuous bag-of-words model [37].”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
The newer paper generalises word-level embeddings to sentences and joint image-sentence spaces, explicitly abstracting the skip-gram model to a higher level.'— the tool’s reading
“Using pretrained word2vec representations learned with a continuous bag-of-words model [8], we learn a linear mapping from a word in word2vec space to a word in the encoder’s vocabulary space.”
“Using word vector learning as inspiration, we propose an objective function that abstracts the skip-gram model of [8] to the sentence level.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Let 𝒱_w2v denote the word embedding space of these word representations and let 𝒱_rnn denote the RNN word embedding space.”
The newer paper carries the word-embedding concept from NLP into a new domain, graph/manifold representation learning, applying similar mapping principles to nodes and vertices.— the tool’s reading
“Most recent approaches [40], [41], [42] tried to apply the successful word embedding model [43] to graphs.”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“However, a Riemannian manifold can be realized as a subset of a Euclidean space (in which case it is said to be embedded in that space) by using the structure of the Euclidean space to induce a Riemannian metric.”
The embedding concept moves from word-vector NLP representations into normalized latent vectors for contrastive visual representation learning, a new modality and task context.— the tool’s reading
“Normalized cross entropy loss with adjustable temperature works better than alternatives We compare the NT-Xent loss against other commonly used contrastive loss functions, such as logistic loss (Mikolov et al., 2013), and margin loss (Schroff et al., 2015).”
“We propose two novel model architectures for computing continuous vector representations of words from very large data sets.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper carries the geometric graph-embedding notion into recommendation systems, redefining embeddings as trainable latent user/item vectors refined via graph convolution.— the tool’s reading
“modeling graph structure, especially high-hop neighbors, to guide the embedding learning [12, 21]. Early studies define graph convolution on the spectral domain, such as Laplacian eigen-decomposition [1] and Chebyshev polynomials [6], which are computationally expensive. Later on, GraphSage [12] and GCN [21] re-define graph convolution in the spatial domain,…”
“One possible option is to consider a global embedding of the graph into a low dimensional space, which is rare in practice for high-dimensional data.”
“The most common paradigm for CF is to learn latent features (a.k.a. embedding) to represent a user and an item, and perform prediction based on the embedding vectors <cit.>.”
The concept shifts from embedding individual nodes into geometric space to producing a whole-graph fixed-length vector via a readout function, applying 'embedding' to a new representational purpose.'— the tool’s reading
“We closely follow the notation defined in Bruna et al. (2013) equation (3.2).”
“The operations defined in Bruna et al. (2013); Defferrard et al. (2016) result in message functions of the form Mt(htv, h t w) = C t vwh t w, where the matrices Ctvw are parameterized by the eigenvectors of the graph laplacian L, and the learned parameters of the model.”
“Another family of models defined in Defferrard et al. (2016), Bruna et al. (2013), Kipf & Welling (2016) can be interpreted as MPNNs.”
“One possible option is to consider a global embedding of the graph into a low dimensional space, which is rare in practice for high-dimensional data.”
“Then, after M steps of computation, the set2set model produces a graph level embedding q_t^* which is invariant to the order of the of the tuples T.”
The generic graph embedding idea is carried into the new domain of multi-relational knowledge graphs for link prediction via GCN-based encoders.'— the tool’s reading
“It is primarily motivated as an adaption of previous work on GCNs (Bruna et al. 2014; Duvenaud et al. 2015; Defferrard, Bresson, and Vandergheynst 2016; Kipf and Welling 2017) for large-scale and highly multi-relational data, characteristic of realistic knowledge bases.”
“It is primarily motivated as an adaption of previous work on GCNs (Bruna et al. 2014; Duvenaud et al. 2015; Defferrard, Bres-son, and Vandergheynst 2016; Kipf and Welling 2017) for large-scale and highly multi-relational data, characteristic of realistic knowledge bases.”
“One possible option is to consider a global embedding of the graph into a low dimensional space, which is rare in practice for high-dimensional data.”
“The encoder maps each entity v_i ∈𝒱 to a real-valued vector e_i ∈ℝ^d.”
The newer paper generalizes shared image-text embedding space into a formal bilinear compatibility framework supporting multiple output embedding types, building on the earlier regression-based approach.— the tool’s reading
“Similarly, [40, 50] use the regression objective and CCA [20] maximizes the correlation of input and output embeddings.”
“Multimodal embeddings relate information from multiple sources such as sound and video <cit.> or images and text.”
“Given a specific input embedding, we derive a prediction by maximizing the compatibility F over SJEas follows: f(x;w) = max_y ∈𝒴 F(x,y; w). The parameter vector w can be written as a D × E matrix W with D being the input embedding dimension and E being the output embedding dimension. This leads to the bi-linear form of the compatibility function: F(x,y…”
The newer paper generalizes the earlier image-to-word-embedding mapping into a broader label-embedding framework with compatibility functions, improving on regression-based optimization criteria.— the tool’s reading
“Possible sources of prior information include attributes [30], [18], [43], [47], [46], semantic class taxonomies [46], [39], class-to-class similarities [47], [70], text features [43], [47], [46], [57], [20] or class co-occurrence statistics [37].”
“We will see in section 5.3 that this leads to improved results compared to those works which optimize a regression criterion such as [43], [57].”
“Multimodal embeddings relate information from multiple sources such as sound and video <cit.> or images and text.”
“We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors.”
The newer paper generalises the earlier single-modality image embedding into a shared multi-modal space aligning images and text, building on the same representational concept.— the tool’s reading
“Figure <ref> shows a two-dimensional embedding of all the ImageNet validation images, colored by a coarse category that they come from. The nice separation testifies to a successful semantic embedding.”
“We introduce a model for bidirectional retrieval of images and sentences through a multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree re…”
The newer paper uses 'embedding' in an unrelated, everyday sense of metadata insertion, diverging from and implicitly disputing the ML representation meaning of the earlier paper.'— the tool’s reading
“Figure <ref> shows a two-dimensional embedding of all the ImageNet validation images, colored by a coarse category that they come from. The nice separation testifies to a successful semantic embedding.”
“While GPS-enabled devices are capable of embedding the precise time, location and orientation of capture in the metadata of a photo, in many instances this information is unavailable or out of date: seconds, hours or sometimes even months.”
The newer paper generalizes single-space image embeddings into a joint image-label compatibility framework, building on the earlier learned representation concept for classification tasks.— the tool’s reading
“For both networks, we used the publicly-available BVLC implementations [23].”
“Figure <ref> shows a two-dimensional embedding of all the ImageNet validation images, colored by a coarse category that they come from. The nice separation testifies to a successful semantic embedding.”
“Given a specific input embedding, we derive a prediction by maximizing the compatibility F over SJEas follows: f(x;w) = max_y ∈𝒴 F(x,y; w). The parameter vector w can be written as a D × E matrix W with D being the input embedding dimension and E being the output embedding dimension. This leads to the bi-linear form of the compatibility function: F(x,y…”
The newer paper shifts embedding from image feature clustering to abstract vector spaces like word embeddings, applying the concept in a new domain of interpretability discussion.— the tool’s reading
“Instead, the techniques of interpretation should apply to a general class of nonlinear machine learning models, no matter how they were trained and who trained them – even fully trained models that are available for download like BVLC CaffeNet [24] or GoogleNet [50]”
“On the BVLC CaffeNet [24], LRP-α2β1 was shown to work well, while for the deeper GoogleNet [50], LRP-α1β0 was found to be more stable.”
“Figure <ref> shows a two-dimensional embedding of all the ImageNet validation images, colored by a coarse category that they come from. The nice separation testifies to a successful semantic embedding.”
“Examples of domains that are not interpretable are abstract vector spaces (e.g. word embeddings <cit.>), or domains composed of undocumented input features (e.g. sequences with unknown words or symbols).”
The newer paper builds on embedding as a representation space by adding interpretability, framing directions within that space as human-understandable concepts rather than just similarity clusters.'— the tool’s reading
“…the learned prediction strategies of our model and to verify that it uses meaningful features in the training data, we take an off-the-shelve explanation software, the LRP Toolbox [83] for Caffe [65], and choose the method LRP configured to perform ‘LRP-(cid:15) ’ on all layers in a first attempt.”
“One of the earlier and comprehensive XAI software packages is the LRP Toolbox [83], providing presently up to date implementations of LRP for the — until very recently — popular Caffe deep learning framework [65], as well as Matlab and Python via custom neural network interfaces.”
“Figure <ref> shows a two-dimensional embedding of all the ImageNet validation images, colored by a coarse category that they come from. The nice separation testifies to a successful semantic embedding.”
“Further methods do not seek to explain in terms of input features, but in terms of the latent space, where the directions in the latent space code for higher-level concepts, such as color, material, object part, or object <cit.>.”
The term 'embedding' shifts from deep-network learned image representations to kernel mean embeddings of distributions in RKHS, applying the concept to a new mathematical/domain-adaptation setting.ed— the tool’s reading
“…ImageNet (Russakovsky et al., 2014) are representative for general-purpose tasks (Yosinski et al., 2014; Hoffman et al., 2014), the proposed DAN model is trained by fine-tuning from the AlexNet model (Krizhevsky et al., 2012) pre-trained on ImageNet, which is implemented in Caffe (Jia et al., 2014).”
“, CNN, LapCNN, DDC, and DAN with the Caffe (Jia et al., 2014) implementation of AlexNet (Krizhevsky et al.”
“, 2012) pre-trained on ImageNet, which is implemented in Caffe (Jia et al., 2014).”
“Figure <ref> shows a two-dimensional embedding of all the ImageNet validation images, colored by a coarse category that they come from. The nice separation testifies to a successful semantic embedding.”
“The mean embedding of distribution p in ℋ_k is a unique element μ_k(p) such that 𝐄_𝐱∼ pf( 𝐱) = ⟨f( 𝐱),μ _k( p )⟩ _ℋ_k for all f ∈ℋ_k.”
The newer paper carries the embedding concept from image feature representation into kernel Hilbert space distribution matching, a distinct mathematical domain and purpose.confidence reflects lack of direct citation context.9— the tool’s reading
“Figure <ref> shows a two-dimensional embedding of all the ImageNet validation images, colored by a coarse category that they come from. The nice separation testifies to a successful semantic embedding.”
“We fuse features of multiple layers with tensor product and embed them into reproducing kernel Hilbert spaces to match distributions for feature adaptation.”
The embedding concept moves from word/tag similarity representations to learned text/image feature vectors used for conditioning a GAN, a new modality and task.'— the tool’s reading
“By conditioning both generator and discriminator on side information (also studied by Mirza & Osindero (2014) and Denton et al. (2015)), we can naturally model this phenomenon since the discriminator network acts as a “smart” adaptive loss function.”
“Conceptual word embeddings <cit.> can be very useful here since related concepts end up being represented by similar vectors.”
“To obtain a visually-discriminative vector representation of text descriptions, we follow the approach of <cit.> by using deep convolutional and recurrent text encoders that learn a correspondence function with images.”
The shared vector-space embedding idea for image-text alignment is carried into a new modality, video-text, using similar ranking-loss training.'— the tool’s reading
“We train our embedding model using the maxmargin ranking loss [21, 32, 54, 55, 64].”
“We introduce a model for bidirectional retrieval of images and sentences through a multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree re…”
“Given this, our goal is to learn two mapping functions: f:ℝ^d_v→ℝ^d and g:ℝ^d_c→ℝ^d that respectively embed video and caption features into a common d-dimensional space, such that the cosine similarity”
The newer paper repurposes 'embedding' to specifically denote dense word-vector layers within a network, narrowing the earlier broader shared image-text vector space concept to a single-modality mechanism.— the tool’s reading
“For the vision part, we use the pre-trained AlexNet (Krizhevsky et al. (2012)) or the VggNet (Simonyan & Zisserman (2014)) on ImageNet dataset (Russakovsky et al.”
“Recently, Karpathy et al. (2014) show that using the RCNN object detection results (Girshick et al. (2014)) combined with the AlexNet features performs better than simply treating the image as a whole frame.”
“For the vision part, we use the pre-trained AlexNet (Krizhevsky et al. (2012)) or the VggNet (Simonyan & Zisserman (2014)) on ImageNet dataset (Russakovsky et al. (2014)).”
“We introduce a model for bidirectional retrieval of images and sentences through a multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree re…”
“The two word embedding layers embed the one-hot input into a dense word representation. It encodes both the syntactic and semantic meaning of the words. The semantically relevant words can be found by calculating the Euclidean distance between two dense word vectors in embedding layers.”
The newer paper extends dense vector embeddings from purely textual domain into a joint image-text multimodal space, carrying the idea into a new modality.— the tool’s reading
“In language domain, several neural network models have been proposed to learn word/n-gram representations [29, 30, 31, 32, 33, 34], sentence representations [35] and paragraph/document representations [36].”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
“We introduce a model for bidirectional retrieval of images and sentences through a multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree re…”
The newer paper carries the vector-embedding idea from NLP semantic representation into computer vision, repurposing it as associative tags for grouping detections.— the tool’s reading
“Works in natural language processing have used vector embeddings to represent the meaning of words, sentences, and paragraphs [30, 24].”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
“The basic idea is to introduce, for each detection, a real number that serves as a “tag” to identify the group the detection belongs to. In other words, the tags associate each detection with other detections in the same group.”
The newer paper generalizes single fixed-length vector embeddings into a multi-hop attention-derived matrix representation, building directly on the earlier dense vector concept.— the tool’s reading
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
The earlier word/document vector embedding concept is carried into a new domain—network nodes—preserving structural and attribute proximity rather than textual semantics.— the tool’s reading
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
“The aim of social network embedding is to project the social actors into a low-dimensional vector space (a.k.a. embedding space).”
The newer paper builds on distributed word/paragraph vector embeddings by applying RNN-based sequential encoding with weak supervision to produce sentence embeddings for a specific task.— the tool’s reading
“Inspired by the word embedding method [4], [5], the authors in [2] proposed an unsupervised learning method to learn a paragraph vector as a distributed representation of sentences and documents, which are then used for sentiment analysis with superior performance.”
“• window = 5 : fixed window size explained in [2].”
“In [2], a paragraph vector is learned in an unsupervised manner as a distributed representation of sentences and documents, which are then used for sentiment analysis.”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
“In text and language processing, one such problem is learning of an embedding vector for a sentence; that is, to train a model that can automatically transform a sentence to a vector that encodes the semantic meaning of the sentence.”
The newer paper carries the dense vector embedding idea from words/documents into a new domain—network vertices—preserving proximity semantics in graphs.— the tool’s reading
“The readers can find advanced document embedding approaches in [7].”
“We categorize the vertices into different groups according to their degrees including (0, 1], [2, 3], [4, 6], [7, 12], [13, 30], [31,+∞), and then evaluate the performance of vertices in different groups.”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
“Given a large network G=(V,E), the problem of Large-scale Information Network Embedding aims to represent each vertex v∈ V into a low-dimensional space R^d, i.e., learning a function f_G:V→ R^d, where d ≪ |V|. In the space R^d, both the first-order proximity and the second-order proximity between the vertices are preserved.”
The embedding-based generative framework from word embeddings is carried into knowledge graph link prediction and recommendation, applying the same core idea to a new domain and structured relational data.— the tool’s reading
“orize multiple relation matrix on a product knowledge graph. As shown in Equation (8), the final objective of the proposed model is controlled by the noisy distribution Pt. Similar to previous studies [30,33,35], we notice that the relationships with tail entities that have high frequency in the collection reveal less information about the properties of the h…”
“0 t, which is exactly the goal of the embedding-based generative framework. The embedding-based generative framework is first proposed by Mikolov et al. [29] and has been widely used in word embedding [29,30], recommendation [31,32], and information retrieval tasks [33,34]. Formally, for an observed relation triplet (e h,et,r) 2S, we can learn the translation…”
“After the training converges, words with similar meaning are mapped to a similar position in the vector space.”
“Inspired by <cit.>, we project each entity and relation into a unified low-dimensional embedding space. Intuitively, the embedding of a tail entity should be close to its translated head entity embedding.”
The newer paper takes joint image-label compatibility embeddings developed for zero-shot recognition and repurposes them as text/image embeddings conditioning GAN-based image generation, a new task domain.eta— the tool’s reading
“, 2014), in particular to enable zero-shot visual recognition (Fu et al., 2014; Akata et al., 2015), and recently for conditional image generation (Yan et al.”
“…the object category encoded into a vector (Farhadi et al., 2009; Kumar et al., 2009; Parikh & Grauman, 2011; Lampert et al., 2014), in particular to enable zero-shot visual recognition (Fu et al., 2014; Akata et al., 2015), and recently for conditional image generation (Yan et al., 2015).”
“As in Akata et al. (2015) and Reed et al. (2016), we split these into class-disjoint training and test sets.”
“Given a specific input embedding, we derive a prediction by maximizing the compatibility F over SJEas follows: f(x;w) = max_y ∈𝒴 F(x,y; w). The parameter vector w can be written as a D × E matrix W with D being the input embedding dimension and E being the output embedding dimension. This leads to the bi-linear form of the compatibility function: F(x,y…”
“To obtain a visually-discriminative vector representation of text descriptions, we follow the approach of <cit.> by using deep convolutional and recurrent text encoders that learn a correspondence function with images.”
Both use 'embedding' for compact dense vector representations, but the newer paper restricts it specifically to a global channel descriptor from pooling within the squeeze-excitation block.— the tool’s reading
“Multi-branch convolutions can be interpreted as a generalisation of this concept, enabling more flexible compositions of operators [16, 42, 43, 44].”
“VGGNets [39] and Inception models [43] demonstrated the benefits of increasing depth.”
“One such approach was popularised by the Inception architectures [16, 43], which showed that the network can achieve competitive accuracy by embedding multi-scale processes in its modules.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“The function of this descriptor is to produce an embedding of the global distribution of channel-wise feature responses, allowing information from the global receptive field of the network to be used by all its layers.”
The embedding concept moves from dense image-patch feature vectors in CNNs to word/tag vector representations learned via skip-gram, a new modality and domain.dyn— the tool’s reading
“Despite the many recent successes of supervised neural networks (and convolutional networks in particular) [13, 17], it remains challenging to scale such models to accommodate an extremely large number of predicted output categories.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“Conceptual word embeddings <cit.> can be very useful here since related concepts end up being represented by similar vectors.”
FaceNet restricts the general dense-vector embedding notion to a specific metric-learning space where L2 distance equals face similarity, a specialized use case.by design.— the tool’s reading
“We would like to thank Johannes Steffens for his discussions and great insights on face recognition and Christian Szegedy for providing new network architectures like [16] and discussing network design choices.”
“On the other hand we have Inception [16] based models that dramatically reduce the model size.”
“The second category we use is based on GoogLeNet style Inception models [16].”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.”
The newer paper builds on the earlier dense feature embedding idea, extending it to a fixed final-layer vector used for retrieval and downstream LSTM input.— the tool’s reading
“We then train a convolutional neural network (CNN) [46] using millions of geotagged images.”
“We train a CNN based on the Inception architecture [46] with batch normalization [20].”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“We extract image embeddings from the final layer below the SoftMax layer (a 2048-dim. vector) and rank images by the Euclidean distance between their embedding vectors.”
The dense vector embedding concept moves from a supervised CNN dimension-reduction context into unsupervised CPC pretraining for representation learning as encoder outputs.'}<br>— the tool’s reading
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
The newer paper builds on dense image feature embeddings from CNNs (used as θ(x)) and generalizes them into a joint image-label compatibility space for zero-shot classification.— the tool’s reading
“For AlexNet (denoted as CNN) we use the 4,096-dim top-layer hidden unit activations (fc7) as features, and for GoogLeNet (denoted as GOOG) we use the 1,024-dim top-layer pooling units.”
“We resize each image to 224×224 and feed into the network which was pre-trained following the model architecture of either AlexNet [27] or GoogLeNet [51] [22].”
“We resize each image to 224×224 and feed into the network which was pre-trained following the model architecture of either AlexNet [27] or GoogLeNet [51, 22].”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“Given a specific input embedding, we derive a prediction by maximizing the compatibility F over SJEas follows: f(x;w) = max_y ∈𝒴 F(x,y; w). The parameter vector w can be written as a D × E matrix W with D being the input embedding dimension and E being the output embedding dimension. This leads to the bi-linear form of the compatibility function: F(x,y…”
The dense vector embedding concept is carried from CNN-based image feature encoding into patch tokenization for transformer architectures, a new modality of use.','confidence':0.55}— the tool’s reading
“We follow DeiT [29] and apply random cropping, random horizontal flipping [27], label-smoothing regularization [28], mixup [36], and random erasing [38] as data augmentations.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“Second, to model the local continuity information, we utilize overlapping patch embedding to tokenize images. As shown in Fig. <ref>(a), we enlarge the patch window, making adjacent windows overlap by half of the area, and pad the feature map with zeros to keep the resolution.”
The dense-vector embedding idea moves from image patch representations (CNN) to word-level vector embeddings in NLP question representation, a new modality/domain.— the tool’s reading
“SMem [22] uses GoogLeNet [20] and the rest all use VGGNet [19], and Ours+VGG outperforms them by 0.”
“SMem [22] uses GoogLeNet [18] and the rest all use VGGNet [17], and Ours+VGG outperforms them by 0.2% on test-dev (DMN+ [21]).”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“Given the 1-hot encoding of the question words Q={q_1,…, q_T }, we first embed the words to a vector space (learnt end-to-end) to get Q^w = {q^w_1,…, q^w_T }.”
The dense vector embedding concept from image feature compression (GoogleNet) is carried into face recognition embeddings via FaceNet/MobileNet distillation.a— the tool’s reading
“Table 8 compares full MobileNet to the original GoogleNet [30] and VGG16 [27].”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“It builds face embeddings based on the triplet loss.”
The newer paper shifts embedding usage from dense image feature vectors to abstract word/kernel embedding spaces, applying the concept in a different domain and context.'}— the tool’s reading
“…matter how they were trained and who trained them – even fully trained models that are available for download like BVLC CaffeNet [24] or GoogleNet [50] In that sense the presented novel technological development in ML allowing for interpretability is an orthogonal strand of research independent…”
“On the BVLC CaffeNet [24], LRP-α 2 β 1 was shown to work well, while for the deeper GoogleNet [50], LRP-α 1 β 0 was found to be more stable.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“Examples of domains that are not interpretable are abstract vector spaces (e.g. word embeddings <cit.>), or domains composed of undocumented input features (e.g. sequences with unknown words or symbols).”
The dense feature-vector embedding concept from convolutional image patches is carried into deep metric learning's embedding spaces for retrieval, a new task domain.','confidence'} ,— the tool’s reading
“Unweighted averaging is a reasonable choice when the performance of the base learners is comparable, as suggested in [13, 123, 124].”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“The training set in the BIER is reweighed via the negative gradient of the loss function to project the input spaces (images) into a collection of independent output spaces. To make BIER more robust, Hierarchical Boosted deep metric learning <cit.> incorporated the hierarchical label information into the embedding ensemble which improves the performance of t…”
The embedding concept is carried from image-patch dimensionality reduction (1x1 convolutions) into a new context of attention mechanisms as a general feature-transform function.confidence reflects limited direct citation evidence.rationale under 35 words.confidence 0.55.rationale ends.confidence.rationale.confidence.rationale.— the tool’s reading
“For instance, GoogleNet [178] conforms to the above formula, but does not belong to the attention mechanisms.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“where ϕ denotes an embedding function used for dimensionally reduction, ϕ(x)^T is the transpose of ϕ(x) along with the channel dimension, Utri(·) is the operator that extracts the upper triangular elements of a matrix and Vec(·) is vectorization.”
The newer paper drops the earlier formal dense-vector-embedding definition, using 'embedding' loosely for network integration, diverging from the original representation-learning meaning cited only for classification benchmarks.'— the tool’s reading
“A standard supervised approach, GoogLeNet (Szegedy et al., 2015), which normally achieves near state-of-the-art performance on the ImageNet validation set, achieves only around 5% top-1 classification accuracy when trained with only 1% of the labeled images, as shown by Pu et al.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“Its learning algorithm is a mix of classical (amortized, variational) expectation maximization but through the reparameterization trick ends up backpropagating through the many layers of the deep neural networks embedded inside of it.”
The general dense feature-vector embedding concept is specialized into a task-specific retrieval embedding trained via NetVLAD pooling for place recognition similarity.— the tool’s reading
“[40,63,64]. In the last few years convolutional neural networks (CNNs) [38,39] have emerged as powerful image representations for various category-level recognition tasks such as object classification [37,49,73,77], scene recognition [91] or object detection [21]. The basic principles of CNNs are known from 80’s [38,39] and the recent successes are a combinat…”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“This is generally done by designing a function f which acts as the “image representation extractor”, such that given an image I_i it produces a fixed size vector f(I_i).”
The dense-vector embedding concept moves from image patch feature encoding (CNN) to word/token representation and image-text alignment in a language decoder context.— the tool’s reading
“For identical decoder architectures, using a more recent architectures such as GoogLeNet (Szegedy et al., 2014) or Oxford VGG (Simonyan & Zisserman, 2014) can give a boost in performance over using the AlexNet (Krizhevsky et al.”
“For identical decoder architectures, us-ing more recent architectures such as GoogLeNet or Oxford VGG Szegedy et al. (2014), Simonyan & Zisserman (2014) can give a boost in performance over using the AlexNet (Krizhevsky et al., 2012).”
“In our evaluation, we compare directly only with results which use the comparable GoogLeNet/Oxford VGG features, but for METEOR comparison we note some results that use AlexNet.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“𝐄∈ℝ^m× K is an embedding matrix.”
The newer paper generalizes dense feature vector embeddings into normalized latent representations specifically optimized for contrastive similarity learning, building on the earlier compact encoding notion.— the tool’s reading
“The other type of augmentation involves appearance transformation, such as color distortion (including color dropping, brightness, contrast, saturation, hue) (Howard, 2013; Szegedy et al., 2015), Gaussian blur, and Sobel filtering.”
“The other type of augmentation involves appearance transformation, such as color distortion (including color dropping, brightness, contrast, saturation, hue) (Howard, 2013; Szegedy et al., 2015), Gaussian blur, and Sobel filtering.”
“Since ImageNet images are of different sizes, we always apply crop and re-size images (Krizhevsky et al., 2012; Szegedy et al., 2015), which makes it difficult to study other augmentations in the absence of cropping.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper extends embeddings from purely dense image feature vectors to text representations aligned with images, applying the concept in a new modality (text-to-image GAN conditioning).— the tool’s reading
“For text features, we first pre-train a deep convolutional-recurrent text encoder on structured joint embedding of text captions with 1,024-dimensional GoogLeNet image embedings (Szegedy et al., 2015) as described in subsection 3.2.”
“For text features, we first pre-train a deep convolutionalrecurrent text encoder on structured joint embedding of text captions with 1,024-dimensional GoogLeNet image embedings (Szegedy et al., 2015) as described in subsection 3.”
“This is based on the success of embeddings: even low dimensional embeddings might contain a lot of information about a relatively large image patch. However, embeddings represent information in a dense, compressed form and compressed information is harder to model.”
“To obtain a visually-discriminative vector representation of text descriptions, we follow the approach of <cit.> by using deep convolutional and recurrent text encoders that learn a correspondence function with images.”
The newer paper reduces the rich semantic embedding concept to a specific engineering operation—lookup table mapping symbols to vectors for TPU inference—rather than extending its semantics.co— the tool’s reading
“In practice, however, NMT systems used to be worse in accuracy than phrase-based translation systems, especially when training on very large-scale datasets as used for the very best publicly available translation systems.”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“such as embedding lookup and attention module, remain on the CPU, and all other quantized operations are off-loaded to the TPU.”
Newer paper generalizes dense word embeddings into fixed-dimensional vector mapping for entire variable-length sequences via LSTM, building on the earlier RNN Encoder-Decoder embedding concept.and application scope.— the tool’s reading
“[5] used an LSTM-like RNN architecture to map sentences into vectors and back, although their primary focus was on integrating their neural network into an SMT system.”
“[5] although the latter was used only for rescoring hypotheses produced by a phrase-based system.”
“[5] by translating pieces of the source sentence in way that produces smooth translations, which is similar to a phrase-based approach.”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“We used deep LSTMs with 4 layers, with 1000 cells at each layer and 1000 dimensional word embeddings, with an input vocabulary of 160,000 and an output vocabulary of 80,000.”
The dense-vector embedding idea from word/phrase representations is carried into a new domain—encoding raw observations into latent predictive representations for signal/mutual information compression.— the tool’s reading
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
The dense embedding concept from word/phrase representation learning is carried into speech synthesis as speaker/style embeddings, applying the vector representation idea to a new modality.'— the tool’s reading
“We also investigate stacked gated recurrent unit (GRU) [17] layers as an alternative for frame-level feature extraction, since they have proven to be effective for speech processing applications [18][19].”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering.”
The newer paper builds directly on the RNN Encoder-Decoder embedding framework of Cho et al., adding an attention mechanism while retaining the same embedding representation concept.— the tool’s reading
“3 As a comparison, we also report the performance of an RNN Encoder–Decoder which was proposed recently by Cho et al. (2014a).”
“Following the procedure described in Cho et al. (2014a), we reduce the size of the combined corpus to have 348M words using the data selection method by Axelrod et al. (2011).”
“Here, we describe briefly the underlying framework, called RNN Encoder–Decoder, proposed by Cho et al. (2014a) and Sutskever et al. (2014) upon which we build a novel architecture that learns to align and translate simultaneously.”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“E∈ℝ^m× K_x is the word embedding matrix.”
Both use learned dense token embeddings; the newer paper generalizes the embedding lookup to a new architecture (Transformer) beyond RNN encoder-decoders.— the tool’s reading
“Recurrent neural networks, long short-term memory [12] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [29, 2, 5].”
“Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 29].”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
The newer paper extends the RNN encoder-decoder's word embedding idea into image captioning, adding a joint image-text embedding space beyond the original text-only context.'— the tool’s reading
“Some that share the same spirit as our work include Larochelle & Hinton (2010); Denil et al. (2012); Tang et al. (2014) and more recently Gregor et al.”
“Some that share the same spirit as our work include Larochelle & Hinton (2010); Denil et al. (2012); Tang et al.”
“Some that share the same spirit as our work include Larochelle & Hinton (2010); Denil et al. (2012); Tang et al. (2014) and more recently Gregor et al. (2015). In particular however, our work directly extends the work of Bahdanau et al.”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“𝐄∈ℝ^m× K is an embedding matrix.”
The newer paper carries the dense embedding idea from word/phrase representations in machine translation into a joint image-text multimodal space, a new modality/domain.'— the tool’s reading
“Current methods include using a convolutional encoder and RNN decoder [8], RNN encoder and RNN decoder [9, 10] and LSTM encoder with LSTM decoder [11].”
“Furthermore, our method builds on analogous approaches being used in machine translation [8, 9, 10, 11].”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
The newer paper generalizes dense vector embeddings from word/phrase representations to sentence embeddings and joint image-sentence spaces, building on the same encoder-decoder framework.— the tool’s reading
“This model combination is nearly identical to the RNN encoder-decoder of [11] used in neural machine translation.”
“Several choices of encoder-decoder pairs have been explored, including ConvNet-RNN [10], RNN-RNN [11] and LSTM-LSTM [12].”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“Let 𝒱_w2v denote the word embedding space of these word representations and let 𝒱_rnn denote the RNN word embedding space.”
Newer paper adopts the same dense embedding concept but applies it within an image captioning RNN architecture, building on the earlier RNN Encoder-Decoder embedding idea.confidence reflects modest citation context detail.— the tool’s reading
“Five people are standing and four are squatting on a brown rock in the foreground; A blue sky in the background; Figure 1: Examples of the generated and two top-ranked retrieved sentences given the query image from IAPR TC-12 dataset.”
“Recently, RNNs have been successfully applied to machine translation to extract semantic information from the source sentence and generate target sentences (e.g. Kalchbrenner & Blunsom (2013), Cho et al. (2014) and Sutskever et al. (2014)).”
“We used rank-100 matrices, equivalent to learning an embedding of dimension 100 for each word.”
“The two word embedding layers embed the one-hot input into a dense word representation. It encodes both the syntactic and semantic meaning of the words. The semantically relevant words can be found by calculating the Euclidean distance between two dense word vectors in embedding layers.”
The newer paper adopts the same lookup-based embedding notion but treats it as a fixed, specific operation for implementation (CPU-based lookup during quantized inference) rather than a general learned representation.— the tool’s reading
“While the translation accuracy of these systems has been encouraging, systematic comparison with large scale, production quality phrase-based translation systems has been lacking.”
“Motivated by the idea of modeling differences between an intermediate layer’s output and the targets, which has shown to work well for many projects in the past [16, 21, 40], we introduce residual connections among the LSTM layers in a stack (see Figure 2).”
“C L ] 8 responsible for this gap: its slower training and inference speed, ineffectiveness in dealing with rare words, and sometimes failure to translate all words in the source sentence.”
“E∈ℝ^m× K_x is the word embedding matrix.”
“such as embedding lookup and attention module, remain on the CPU, and all other quantized operations are off-loaded to the TPU.”
The dense vector embedding concept from NLP word representations is carried into speech synthesis to represent speaker/style characteristics instead of words.— the tool’s reading
“Tacotron 2 [15] used WaveNet [19] as a vocoder to invert spectrograms generated by an encoderdecoder architecture with attention [3], obtaining naturalness approaching that of human speech by combining Tacotron’s [23] prosody with WaveNet’s audio quality.”
“E∈ℝ^m× K_x is the word embedding matrix.”
“a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech without transcripts from thousands of speakers, to generate a fixed-dimensional embedding vector from only seconds of reference speech from a target speaker;”
The newer paper generalizes word-level embeddings into sentence-level fixed vectors using RNN hidden states, building on the earlier encoder-decoder embedding mechanism for a new representational scope.'— the tool’s reading
“In [19], an encoder-decoder approach is proposed to jointly learn to align and translate sentences from English to French using RNNs.”
“E∈ℝ^m× K_x is the word embedding matrix.”
“In text and language processing, one such problem is learning of an embedding vector for a sentence; that is, to train a model that can automatically transform a sentence to a vector that encodes the semantic meaning of the sentence.”
The dense-vector embedding concept from neural machine translation is carried into recommendation systems as latent user/item factor vectors, a new domain and modality of application.9— the tool’s reading
“learning has made massive strides in many research areas obtaining state of the art performance in computer vision [9], question answering [18, 30, 35, 39], learning programs [8], machine translation [1] and many other domains. The successful integration of deep learning methods in recommendation systems have demonstrated the noticeable advantages of complex…”
“ressing finds a scoring function between the given question (query) and a passage of text, typically the inner product followed by the softmax operation leading to softly reading each memory location [1, 18, 30, 35, 39]. Performing a soft read over the memory locations allows the model to maintain differentiation hence can be trained via backpropagation. The…”
“ll information in a given task but focus on specific subsets of information. Attention mechanisms also provide a level of insight into the deep learning black box by visualizing the attention weights [1]. Kumar et al. [18] improve upon the existing architecture by introducing an episodic memory component allowing for multiple passes or consultations of the m…”
“E∈ℝ^m× K_x is the word embedding matrix.”
“We use the terms user/item latent factors, memories and embeddings interchangeably.”
The newer paper carries the RNN word-embedding idea from machine translation into a joint image-text multimodal space, applying it to a new domain and modality.— the tool’s reading
“Current methods include using a convolutional encoder and RNN decoder [8], RNN encoder and RNN decoder [9, 10] and LSTM encoder with LSTM decoder [11].”
“Furthermore, our method builds on analogous approaches being used in machine translation [8, 9, 10, 11].”
“E∈ℝ^m× K_x is the word embedding matrix.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
The newer paper carries the dense-vector embedding idea from word/RNN representations into knowledge graph entities and relations, a new domain and modality.'— the tool’s reading
“The attention mechanism was originally proposed in image classification [18] and machine translation [1], which aims to learn where to find the most relevant part of the input automatically as it is performing the task.”
“E∈ℝ^m× K_x is the word embedding matrix.”
“KGE intends to embed entities and relations in a KG into continuous vector spaces while preserving its inherent structure.”
The newer paper restricts 'embedding' to the specific discrete lookup operation used in NMT/TPU inference, a special case of the earlier broader dense-vector representation concept.— the tool’s reading
“While the translation accuracy of these systems has been encouraging, systematic comparison with large scale, production quality phrase-based translation systems has been lacking.”
“C L ] 8 responsible for this gap: its slower training and inference speed, ineffectiveness in dealing with rare words, and sometimes failure to translate all words in the source sentence.”
“For notation, we use bold lower case to denote vectors (e.g., v,oi), bold upper case to represent matrices (e.g., U,W), cursive upper case to represent sets (e.g., V ,T ), capital letters to represent sequences (e.g.”
“We used deep LSTMs with 4 layers, with 1000 cells at each layer and 1000 dimensional word embeddings, with an input vocabulary of 160,000 and an output vocabulary of 80,000.”
“such as embedding lookup and attention module, remain on the CPU, and all other quantized operations are off-loaded to the TPU.”
The newer paper applies the general LSTM-based embedding notion specifically to whole-sentence representations learned via click-through data for query/document similarity, a specialized application domain.— the tool’s reading
“By mapping texts into a unified semantic representation, the embedding vector can be further used for different language processing applications, such as machine translation [1], sentiment analysis [2], and information retrieval [3].”
“In the machine translation work [1], an input English sentence is converted into a vector representation using LSTM-RNN, and then another LSTM-RNN is used to generate an output French sentence.”
“We used deep LSTMs with 4 layers, with 1000 cells at each layer and 1000 dimensional word embeddings, with an input vocabulary of 160,000 and an output vocabulary of 80,000.”
“In text and language processing, one such problem is learning of an embedding vector for a sentence; that is, to train a model that can automatically transform a sentence to a vector that encodes the semantic meaning of the sentence.”
The earlier LSTM-based sequence embedding is carried into a new multimodal setting, jointly embedding images and text rather than just language sequences.— the tool’s reading
“Current methods include using a convolutional encoder and RNN decoder [8], RNN encoder and RNN decoder [9, 10] and LSTM encoder with LSTM decoder [11].”
“LSTMs have been used to achieve state-of-the-art performance in several tasks such as handwriting recognition [31], sequence generation [32] speech recognition [33] and machine translation [11] among others.”
“Furthermore, our method builds on analogous approaches being used in machine translation [8, 9, 10, 11].”
“We used deep LSTMs with 4 layers, with 1000 cells at each layer and 1000 dimensional word embeddings, with an input vocabulary of 160,000 and an output vocabulary of 80,000.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
The newer paper carries the dense-vector embedding idea from word/sequence representations into graph and manifold representation learning domains.','}— the tool’s reading
“This has led to qualitative breakthroughs on a wide variety of tasks, from speech recognition [2], [3] and machine translation [4] to image analysis”
“We used deep LSTMs with 4 layers, with 1000 cells at each layer and 1000 dimensional word embeddings, with an input vocabulary of 160,000 and an output vocabulary of 80,000.”
“However, a Riemannian manifold can be realized as a subset of a Euclidean space (in which case it is said to be embedded in that space) by using the structure of the Euclidean space to induce a Riemannian metric.”
The newer paper builds on fixed word embeddings by generating them dynamically via character-level CNNs instead of learning per-word vectors independently.4— the tool’s reading
“…recently proposed sequence-to-sequence models employ conditional language models (Mikolov & Zweig, 2012) as their key component to solve diverse tasks like machine translation (Sutskever et al., 2014; Cho et al., 2014; Kalch-brenner et al., 2014) or video generation (Srivastava et al., 2015a).”
“Lastly, recently proposed sequence-to-sequence models employ conditional language models as their key component to solve diverse tasks like machine translation (Sutskever et al., 2014; Cho et al., 2014) or video generation (Srivastava et al.”
“We used deep LSTMs with 4 layers, with 1000 cells at each layer and 1000 dimensional word embeddings, with an input vocabulary of 160,000 and an output vocabulary of 80,000.”
“The logit is generally computed as an inner product z_w = h^Te_w where h is a context vector and e_w is a “word embedding” for w.”
The newer paper generalizes the earlier 2D visualization embedding into a probabilistic encoder producing latent codes for broader coding and representation purposes.— the tool’s reading
“We project the MNIST data set to a 2-dimensional latent space and use this 2D embedding as a visualisation of the data – an embedding for MNIST is shown in figure <ref>. The classes separate into different regions, suggesting that such embeddings can be useful in understanding the structure of high-dimensional data sets.”
“From a coding theory perspective, the unobserved variables 𝐳 have an interpretation as a latent representation or code. In this paper we will therefore also refer to the recognition model q_ϕ(𝐳|𝐱) as a probabilistic encoder, since given a datapoint 𝐱 it produces a distribution (e.g. a Gaussian) over the possible values of the code 𝐳 from which the datap…”
The same shared embedding space with ranking loss and cosine similarity is carried from image-text pairs into the video-text modality domain.— the tool’s reading
“Our off-the-shelf HowTo100M model outperforms [22, 24, 53, 64, 65] that are directly trained on MSR-VTT.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
“Given this, our goal is to learn two mapping functions: f:ℝ^d_v→ℝ^d and g:ℝ^d_c→ℝ^d that respectively embed video and caption features into a common d-dimensional space, such that the cosine similarity”
The newer paper restricts 'embedding' to single-modality image feature vectors, dropping the joint image-text shared space aspect of the earlier concept.— the tool’s reading
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
The newer paper adopts embedding primarily as a simpler word-to-vector mapping for LSTM input, narrowing the earlier joint image-text ranking space to a more restricted, task-specific usage.— the tool’s reading
“This work was later followed by Kiros et al. (2014b) whose method was designed to explicitly allow a natural way of doing both ranking and generation.”
“Unlike Kiros et al. (2014a) and Mao et al. (2014) whose models see the image at each time step of the output word sequence, Vinyals et al. (2014) only show the image to the RNN at the beginning.”
“The first approach to use neural networks for caption generation was Kiros et al. (2014a), who proposed a multimodal log-bilinear model that was biased by features from the image.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
“𝐄∈ℝ^m× K is an embedding matrix.”
The newer paper reuses 'embedding' mainly for dense word vector layers, restricting the earlier joint image-sentence space to a special-case reference rather than its main use.— the tool’s reading
“For the sentence retrieval task, Kiros et al. (2014b) uses a shortlist of 100 images which are the nearest neighbors of the query image in the feature space.”
“More closely related to our tasks and method is the work of Kiros et al. (2014b), which is built on a Log-BiLinear model (Mnih & Hinton (2007) 2014)).”
“This strategy leads to a much better performance than that in Kiros et al. (2014b) in the experiments.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
“The two word embedding layers embed the one-hot input into a dense word representation. It encodes both the syntactic and semantic meaning of the words. The semantically relevant words can be found by calculating the Euclidean distance between two dense word vectors in embedding layers.”
The newer paper generalizes the shared image-text embedding idea into a broader text embedding φ(t) usable for GAN conditioning, building on the earlier instance-level matching approach.— the tool’s reading
“However, we can still learn an instance level (rather than category level) image and text matching function, as in (Kiros et al., 2014).”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
“To obtain a visually-discriminative vector representation of text descriptions, we follow the approach of <cit.> by using deep convolutional and recurrent text encoders that learn a correspondence function with images.”
The newer paper applies the earlier shared image-text embedding idea to a new task, VQA, combining image and question embeddings rather than image-sentence pairs for captioning/ranking.— the tool’s reading
“In particular, research in image and video captioning that combines Computer Vision (CV), Natural Language Processing (NLP), and Knowledge Representation & Reasoning (KR) has dramatically increased in the past year [13, 7, 9, 32, 21, 19, 45].”
“Related to VQA are the tasks of image tagging [8, 23], image captioning [24, 14, 34, 7, 13, 45, 9, 19, 32, 21] and video captioning [39, 17], where words or sentences are generated to describe visual content.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
“This channel provides an embedding for the image. We experiment with two embeddings –”
SE-Net reuses 'embedding' for a pooled channel-wise descriptor within a network layer, a specific restricted use versus the earlier generic whole-image feature vector concept.— the tool’s reading
“VGGNets [35] and Inception models [39] demonstrated the benefits that could be attained with an increased depth, significantly outperforming previous approaches on ILSVRC 2014.”
“VGGNets [39] and Inception models [43] demonstrated the benefits of increasing depth.”
“We also assess the effect of SE blocks when operating on non-residual networks by conducting experiments with the VGG-16 [39] and BN-Inception architecture [16].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“The function of this descriptor is to produce an embedding of the global distribution of channel-wise feature responses, allowing information from the global receptive field of the network to be used by all its layers.”
The newer paper generalizes fixed ConvNet feature vectors into learned embeddings from generative encoders, broadening the representation learning framework for downstream tasks.— the tool’s reading
“Learning good image representations is a key challenge in computer vision [1, 2, 3] as it allows for efficient training on downstream tasks [4, 5, 6, 7].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
The embedding concept moves from dense image feature vectors (visual domain) to learned word/question vector representations (language domain), a new modality application.— the tool’s reading
“SMem [22] uses GoogLeNet [20] and the rest all use VGGNet [19], and Ours+VGG outperforms them by 0.”
“SMem [22] uses GoogLeNet [18] and the rest all use VGGNet [17], and Ours+VGG outperforms them by 0.2% on test-dev (DMN+ [21]).”
“Following [23], we rescale the image to 448× 448, and then take the activation from the last pooling layer of VGGNet [19] or ResNet [7] as its feature.”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“Given the 1-hot encoding of the question words Q={q_1,…, q_T }, we first embed the words to a vector space (learnt end-to-end) to get Q^w = {q^w_1,…, q^w_T }.”
The newer paper builds on the earlier notion of dense feature embeddings by adding contrastive learning, normalization, and dictionary-lookup framing for representation learning.— the tool’s reading
“RelativePosition [13] R50w2 94 51.4 [38] Jigsaw [45] R50w2 All are fine-tuned for 24k iterations (94 44.6 [38] Rotation [19] Rv50w4 86 55.4 [38] Colorization [64] R101 28 39.6 [14] DeepCluster [3] VGG [53] 15 48.4 [4] BigBiGAN [16] R50 24 56.6 Rv50w4 86 61.3 methods based on contrastive learning follow: InstDisc [61] R50 24 54.0 LocalAgg [66] R50 24 58.8 CPC …”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
The generic image-embedding concept is carried into face recognition domain via FaceNet's triplet-loss embeddings, distilled into MobileNet, rather than extending the original method.'— the tool’s reading
“The general trend has been to make deeper and more complicated networks in order to achieve higher accuracy [27, 31, 29, 8].”
“Table 8 compares full MobileNet to the original GoogleNet [30] and VGG16 [27].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“It builds face embeddings based on the triplet loss.”
The newer paper repurposes the embedding notion from a whole-image descriptor to an internal channel-wise feature aggregation mechanism within a network for perceptual loss, applying it in a new architectural context.— the tool’s reading
“It is known that the nonlinear transformation generally improves the performance of deep models [44].”
“Specifically, the (cid:96) 2 loss measures the difference between the reconstructed result ˆ J and corresponding ground truth J as: The perceptual loss is computed based on the VGG-19 network φ [44] pre-trained on the ImageNet dataset [51].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“We first perform the global average pooling on input features ℱ, leading to a channel descriptor 𝐳∈ℝ^N× 1, which is an embedded global distribution of channel-wise feature responses.”
The newer paper restricts the generic image embedding concept to a discriminative, identity-based pedestrian descriptor for retrieval via similarity constraints, a specialized domain application.— the tool’s reading
“(For Res50Net [7] and VGG16Net [18], we crop images 224 by 224.)”
“The ImageNet pre-trained CNN model can be CaffeNet [11], VGG16Net [18] or Res50Net [7], from which we have removed the final FC layer.”
“Training: We use the Matconvnet [25] package for training and testing the embedding with Caffenet [11], VGG16net [18] and Res50net [7] respectively.”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“Our network learns a discriminative embedding and a similarity measurement at the same time, thus making full usage of the re-ID annotations.”
The newer paper repurposes dense activation vectors from VGG-16 as a substrate for concept-direction analysis in explainability, moving embeddings into interpretability research rather than image retrieval.— the tool’s reading
“3 (left) for the well-established VGG-16 network [136]: The network is fed multiple consecutive video frames of an athlete lifting a barbell, and we observe the prediction for the output neuron ‘barbell’.”
“Images are correctly predicted by the VGG-16 [136] neural network, and shown along with an explanation of the predictions.”
“This procedure can be applied to simple sequential structures such as VGG-16 [136].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“Further methods do not seek to explain in terms of input features, but in terms of the latent space, where the directions in the latent space code for higher-level concepts, such as color, material, object part, or object <cit.>.”
The newer paper builds on dense image embeddings by applying deep metric learning and ensembling techniques to learned embedding spaces for retrieval tasks.— the tool’s reading
“Unweighted averaging is a reasonable choice when the performance of the base learners is comparable, as suggested in [13, 133, 134].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“The training set in the BIER is reweighed via the negative gradient of the loss function to project the input spaces (images) into a collection of independent output spaces. To make BIER more robust, Hierarchical Boosted deep metric learning <cit.> incorporated the hierarchical label information into the embedding ensemble which improves the performance of t…”
The newer paper generalizes image-embedding notion to unsupervised text/image representation learning via contrastive prediction, extending the concept into a new theoretical framework and modality.— the tool’s reading
“For CIFAR-100 we use VGG-16 (Simonyan & Zisserman, 2014) with an additional 512x100 linear layer added at the end to make the final representations 100 dimensional, while for Wiki-3029 we use a Gated Recurrent Network (GRU) (Chung et al., 2015) with output dimension 300 and fix the word embedding…”
“For CIFAR-100 we use VGG-16 (Simonyan & Zisserman, 2014) with an additional 512x100 linear layer added at the end to make the final representations 100 dimensional, while for Wiki-3029 we use a Gated Recurrent Network (GRU) (Chung et al., 2015) with output dimension 300 and fix the word embedding layer with pretrained GloVe embeddings (Pennington et al., 2014)…”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“In natural language processing (NLP), low-dimensional representations of text – called text embeddings – have been computed with unlabeled data <cit.>. Often the embedding function is trained by using the embedding of a piece of text to predict the surrounding text <cit.>.”
The newer paper takes the earlier image-only embedding representation and carries it into a cross-modal, joint image-text vector space for retrieval tasks.— the tool’s reading
“Let φ ( i ; θ φ ) ∈ R D φ be the representation of the image (e.g. the representation before logits in VGG19 Simonyan & Zisserman (2014) or ResNet152 He et al. (2016)).”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“Such embeddings entail mappings from two (or more) domains into a common vector space in which semantically associated inputs (e.g., text and images) are mapped to similar locations. The embedding space thus represents the underlying domain structure, where location and often direction are semantically meaningful.”
The dense-vector embedding idea from image ConvNet features is carried into graph-structured node representations for a different modality and task (recommendation).}— the tool’s reading
“The visual embeddings are the 6-th fully connected layer of a classification network using the VGG-16 architecture [28].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“We develop a data-efficient Graph Convolutional Network (GCN) algorithm RW-GCN, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information.”
The newer paper builds on ConvNet feature embeddings by adding projection heads and contrastive objectives, generalizing the earlier fixed descriptor use to learned similarity spaces.olate.— the tool’s reading
“Modeling invariances in features is a well studied concept in computer vision with decades of research [48] and plays a critical role in hand-designed features such as SIFT [45], HOG [8], and learned representations from ConvNets [37, 40, 69].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
The newer paper builds on ConvNet activation embeddings by adding end-to-end trainable NetVLAD pooling for the specific retrieval task, generalizing the earlier fixed-descriptor approach.— the tool’s reading
“on ranked database images before evaluation. Implementation details. We use two base architectures which are extended with Max pooling (f max) and our NetVLAD (f VLAD) layers: AlexNet [37] and VGG-16 [73]; both are cropped at the last convolutional layer (conv5), before ReLU. For NetVLAD we use K= 64 resulting in 16k and 32k-D image representations for the t…”
“[40,63,64]. In the last few years convolutional neural networks (CNNs) [38,39] have emerged as powerful image representations for various category-level recognition tasks such as object classification [37,49,73,77], scene recognition [91] or object detection [21]. The basic principles of CNNs are known from 80’s [38,39] and the recent successes are a combinat…”
“t conv5, the baselines either use Max pooling (f max), or aggregate the descriptors into VLAD (f VLAD), but perform no further task-specific training. The three base networks are: AlexNet [37], VGG-16 [73], both are pretrained for ImageNet classification [18], and Places205 [91], reusing the same architecture as AlexNet but pretrained for scene classification […”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“This is generally done by designing a function f which acts as the “image representation extractor”, such that given an image I_i it produces a fixed size vector f(I_i).”
The newer paper carries the general notion of dense learned embeddings from image ConvNet features into word/text representations and joint image-text spaces, applying it to a new modality.— the tool’s reading
“For identical decoder architectures, us-ing more recent architectures such as GoogLeNet or Oxford VGG Szegedy et al. (2014), Simonyan & Zisserman (2014) can give a boost in performance over using the AlexNet (Krizhevsky et al., 2012).”
“To create the annotations a i used by our decoder, we used the Oxford VGGnet (Simonyan & Zisserman, 2014) pre-trained on ImageNet without finetuning.”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“𝐄∈ℝ^m× K is an embedding matrix.”
The newer paper builds on dense image embeddings from ConvNets, extending them into a joint multimodal space with text via learned projections and ranking loss.— the tool’s reading
“We used two convolutional network architectures for extracting 4096 dimensional image features: the Toronto ConvNet 5 as well as the 19-layer OxfordNet [43] which finished 2nd place in the ILSVRC 2014 classification competition.”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.”
The newer paper carries the dense vector 'embedding' concept from image features into word, sentence, and joint image-text vector spaces, extending its use to language modality.ed— the tool’s reading
“For our experiments, we represent images using 4096-dimensional OxfordNet features from their 19-layer model [35].”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“Let 𝒱_w2v denote the word embedding space of these word representations and let 𝒱_rnn denote the RNN word embedding space.”
The dense-image-vector notion of embedding is carried into graph/manifold representation learning, applying similar low-dimensional mapping ideas to a new domain rather than extending or contesting it.— the tool’s reading
“and computer vision [5], [6], [7], [8], [9], [10], [11] (the reader is referred to [12], [13] for many additional examples of successful applications of deep learning).”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“However, a Riemannian manifold can be realized as a subset of a Euclidean space (in which case it is said to be embedded in that space) by using the structure of the Euclidean space to induce a Riemannian metric.”
The newer paper carries the embedding concept from dense image feature vectors into word representation vectors for language, a new modality/domain.— the tool’s reading
“Our method with VggNet image representation (Simonyan & Zisserman (2014)) outperforms the state-of-the-art methods, including the very recently released methods, in almost all the evaluation metrics.”
“Recently, Simonyan & Zisserman (2014) propose a CNN with over 16 layers (denoted as VggNet) and performs substantially better than the AlexNet.”
“For the vision part, we use the pre-trained AlexNet (Krizhevsky et al. (2012)) or the VggNet (Simonyan & Zisserman (2014)) on ImageNet dataset (Russakovsky et al. (2014)).”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“The two word embedding layers embed the one-hot input into a dense word representation. It encodes both the syntactic and semantic meaning of the words. The semantically relevant words can be found by calculating the Euclidean distance between two dense word vectors in embedding layers.”
Both use embeddings as learned dense vector representations of images, but the newer paper generalizes this by adding projection, L2-normalization, and contrastive loss framing for representation learning.— the tool’s reading
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper adopts CNN-derived image embeddings from the earlier work but extends the concept to also encode questions and fuse modalities for VQA.— the tool’s reading
“the last hidden layer of VGGNet [41] as our 4096-dim feature.”
“Image: We use Open-Answer Multiple-Choice All Yes/No Number Other All Yes/No Number Other Question 48.09 75.66 36.70 27.14 53.68 75.71 37.05 38.64 Image 28.13 64.01 00.42 03.77 30.53 69.87 00.45 03.76 Q+I 52.64 75.55 33.67 37.37 58.97 75.59 34.35 50.33 LSTM Q 48.76 78.20 35.68 26.59 54.75 78.22 36.82 38.78 LSTM Q+I 53.74 78.94 35.24 36.42 57.17 78.95 35.80 4…”
“we remove the last fully-connected layer (which performs 1000-way ILSVRC classification), and use 4096-D activations of the penultimate layer as image features, which are aggregated across multiple locations and scales.”
“This channel provides an embedding for the image. We experiment with two embeddings –”
The newer paper carries the word-embedding concept from purely textual word-context learning into a cross-modal image-label compatibility framework for zero-shot classification.confidence— the tool’s reading
“An assumption underlying the embedding process is the following: Assumption maximizing objective <ref> will result in good embeddings v_w ∀ w∈ V, in the sense that similar words will have similar vectors.”
“Given a specific input embedding, we derive a prediction by maximizing the compatibility F over SJEas follows: f(x;w) = max_y ∈𝒴 F(x,y; w). The parameter vector w can be written as a D × E matrix W with D being the input embedding dimension and E being the output embedding dimension. This leads to the bi-linear form of the compatibility function: F(x,y…”
The newer paper generalizes single-vector sentence embeddings into a 2-D matrix via multi-hop self-attention, building directly on the earlier neural sentence embedding concept.— the tool’s reading
“To focus specifically on the strengths of these models at producing informative sentence representations, we use sentence embedding as an intermediate step in the NLI classification task: each model must produce a vector representation of each of the two sentences without using any context from the other sentence, and the two resulting vectors are then passe…”
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
The newer paper generalizes sentence embeddings from monolingual NLI classifiers to cross-lingual pretrained representations, broadening the same core embedding concept to multilingual alignment tasks.— the tool’s reading
“In this context, a Transformer [38] language model is learned on a large unsupervised text corpus, and then fine-tuned on natural language understanding (NLU) tasks such as classification [35] or natural language inference [7, 42].”
“To focus specifically on the strengths of these models at producing informative sentence representations, we use sentence embedding as an intermediate step in the NLI classification task: each model must produce a vector representation of each of the two sentences without using any context from the other sentence, and the two resulting vectors are then passe…”
“suggest that it is possible to build universal cross-lingual encoders that can encode any sentence into a shared embedding space.”
ELMo generalizes static word/sentence embeddings into context-dependent vectors derived from biLM layers, building on earlier fixed embeddings used for NLI baselines like Bowman et al. 2015.— the tool’s reading
“The augmented con-Model Feature based (Bowman et al., 2015) 78.2 DIIN (Gong et al., 2018) 88.0 BCN+Char+CoVe (McCann et al., 2017) 88.1 ESIM (Chen et al., 2017) 88.0 ESIM+TreeLSTM (Chen et al., 2017) 88.6 ESIM+ELMo 88.7 ± 0.17 DIIN ensemble (Gong et al., 2018) 88.9 ESIM+ELMo ensemble 89.3 text…”
“The Stanford Natural Language Inference (SNLI) corpus (Bowman et al., 2015) provides approximately 550K hypothesis/premise pairs.”
“To focus specifically on the strengths of these models at producing informative sentence representations, we use sentence embedding as an intermediate step in the NLI classification task: each model must produce a vector representation of each of the two sentences without using any context from the other sentence, and the two resulting vectors are then passe…”
“Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence.”
The newer paper drops the earlier disentangled-latent-code embedding notion, using 'embedding' only loosely for network integration, implicitly rejecting its formal representational meaning.— the tool’s reading
“In (Kulkarni et al., 2015) (Deep convolutional inverse graphics network), a convolutional VAE was applied to modeling images with some success, building on work by (Dosovitskiy et al.”
“The decoder network is a function that transform a compact graphics code ( 200 dimensions) to a 150×150 image.”
“Its learning algorithm is a mix of classical (amortized, variational) expectation maximization but through the reparameterization trick ends up backpropagating through the many layers of the deep neural networks embedded inside of it.”
The newer paper carries FaceNet's embedding-and-triplet-loss framework from face images into speech/speaker representation, adapting it to audio-based speaker verification and clustering.— the tool’s reading
“We seek to make updates such that the cosine similarity between the anchor and the positive example is larger than the cosine similarity between the anchor and the negative example [11].”
“To avoid suboptimal local minima early-on in training, [11] proposed using semi-hard negative exemplars, as they are further away from the anchor than the positive exemplar, but still hard because the AN cosine similarity is close to the AP cosine similarity.”
“We model the probability of embeddings x i and x j belonging to the same speaker by their cosine similarity in Equation (4), allowing us to use the triplet loss function like in FaceNet [11].”
“In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.”
“We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering.”
The same distance-based embedding concept from face verification is carried into place recognition/retrieval, using NetVLAD pooling instead of face-specific CNN features.'— the tool’s reading
“Note that the above loss is related to the commonly used triplet loss [66, 67, 84, 85], but adapted to our weakly supervised scenario using a formulation (given by equation (5)) similar to multiple instance learning [21, 36, 83].”
“In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.”
“This is generally done by designing a function f which acts as the “image representation extractor”, such that given an image I_i it produces a fixed size vector f(I_i).”
The newer paper generalizes embeddings from margin-based triplet distances to L2-normalized projections optimized via contrastive NT-Xent loss, building on the earlier distance-metric embedding concept.— the tool’s reading
“Normalized cross entropy loss with adjustable temperature works better than alternatives We compare the NT-Xent loss against other commonly used contrastive loss functions, such as logistic loss (Mikolov et al., 2013), and margin loss (Schroff et al., 2015).”
“As a result, one must apply semi-hard negative mining (Schroff et al., 2015) for these loss functions: instead of computing the gradient over all loss terms, one can compute the gradient using semi-hard negative terms ( i.e. , those that are within the loss margin and closest in distance, but…”
“In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper moves the embedding concept from knowledge-graph link prediction into distributional word semantics, citing holographic embeddings as one KG embedding method feeding ConceptNet Numberbatch construction.'}— the tool’s reading
“Holographic embeddings (Nickel, Rosasco, and Poggio 2016) are embeddings learned from a labeled knowledge graph, under the constraint that a circular correlation of these embeddings gives a vector representing a relation.”
“Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.”
“Word embeddings represent words as dense unit vectors of real numbers, where vectors that are close together are semantically related.”
The newer paper generalizes the embedding beyond place-retrieval-specific NetVLAD pooling to a generic CNN feature used across tasks like LSTM input and near-duplicate detection.rez— the tool’s reading
“This is generally done by designing a function f which acts as the “image representation extractor”, such that given an image I_i it produces a fixed size vector f(I_i).”
“We extract image embeddings from the final layer below the SoftMax layer (a 2048-dim. vector) and rank images by the Euclidean distance between their embedding vectors.”
The newer paper repurposes 'embedding' from a concrete learned dense representation into an abstract example domain for interpretability discussion, shifting its application context.dd— the tool’s reading
“These models are however are either limited in their representational power [54, 17], or they require an added level of complexity, for example, an attention mechanism [37, 73].”
“Post-hoc interpretability should be contrasted to incorporating interpretability directly into the structure of the model, as done, for example, in [54, 17, 76, 37, 73].”
“𝐄∈ℝ^m× K is an embedding matrix.”
“Examples of domains that are not interpretable are abstract vector spaces (e.g. word embeddings <cit.>), or domains composed of undocumented input features (e.g. sequences with unknown words or symbols).”
The newer paper generalizes sentence embedding from a single fixed vector to a 2-D matrix via self-attention, building directly on prior single-vector sentence embedding methods.— the tool’s reading
“This includes SkipThought vectors (Kiros et al., 2015), ParagraphVector (Le & Mikolov, 2014), recursive auto-encoders (Socher et al., 2011; 2013), Sequential Denoising Autoencoders (SDAE), FastSent (Hill et al., 2016), etc.”
“Let 𝒱_w2v denote the word embedding space of these word representations and let 𝒱_rnn denote the RNN word embedding space.”
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
The newer paper restricts the general embedding concept specifically to fixed-size RNN-based sentence embeddings trained via weak supervision, a special case of the broader distributed representation notion.— the tool’s reading
“In the second experiment, we used the Skip-Thought vectors proposed in [6].”
“In [6], an unsupervised sentence embedding method is proposed with great performance on large corpus of contiguous text corpus, e.g., the BookCorpus [7].”
“Let 𝒱_w2v denote the word embedding space of these word representations and let 𝒱_rnn denote the RNN word embedding space.”
“In text and language processing, one such problem is learning of an embedding vector for a sentence; that is, to train a model that can automatically transform a sentence to a vector that encodes the semantic meaning of the sentence.”
The newer paper generalizes the latent-space embedding notion beyond GAN noise vectors to context-encoder features, applying the same analogy to a broader representation-learning setting.— the tool’s reading
“In the context of evaluating learned representations of words <cit.> demonstrated that simple arithmetic operations revealed rich linear structure in representation space. One canonical example demonstrated that the vector("King") - vector("Man") + vector("Woman") resulted in a vector whose nearest neighbor was the vector for Queen. We investigated whether s…”
“This is similar in spirit to word2vec <cit.> which learns word representation from natural language sentences by predicting a word given its context.”
The newer paper builds on the DCGAN latent embedding idea by adding an explicit encoder (BiGAN/BigBiGAN) to learn richer, higher-quality semantic embeddings for downstream classification.atur— the tool’s reading
“However, it used a DCGAN [26] style generator, incapable of producing high-quality images on this dataset, so the semantics the encoder could model were in turn quite limited.”
“Other approaches include colorization [34, 35], motion segmentation [25], rotation prediction [9, 2], GAN-based discrimination [26, 2], and exemplar matching [6].”
“In the context of evaluating learned representations of words <cit.> demonstrated that simple arithmetic operations revealed rich linear structure in representation space. One canonical example demonstrated that the vector("King") - vector("Man") + vector("Woman") resulted in a vector whose nearest neighbor was the vector for Queen. We investigated whether s…”
“The encoder ℰ models the inverse conditional distribution P(𝐳|𝐱), predicting latents 𝐳 given data 𝐱 sampled from the data distribution P_𝐱.”
The embedding concept moves from latent noise-vector space arithmetic in image generation to learned text/image embeddings used for GAN conditioning, a new modality and purpose.— the tool’s reading
“Radford et al. (2016) used a standard convolutional decoder, but developed a highly effective and stable architecture incorporating batch normalization to achieve striking image synthesis results.”
“In the context of evaluating learned representations of words <cit.> demonstrated that simple arithmetic operations revealed rich linear structure in representation space. One canonical example demonstrated that the vector("King") - vector("Man") + vector("Woman") resulted in a vector whose nearest neighbor was the vector for Queen. We investigated whether s…”
“To obtain a visually-discriminative vector representation of text descriptions, we follow the approach of <cit.> by using deep convolutional and recurrent text encoders that learn a correspondence function with images.”
The newer paper cites the earlier only for its dataset, while reusing the term 'embedding' with an entirely different meaning, shifting it from metadata insertion to learned vector representations in ML. — the tool’s reading
“To study how much changes in the data distribution impact PIRL, we pre-train models on uncurated images from the unlabeled YFCC dataset [72].”
“While GPS-enabled devices are capable of embedding the precise time, location and orientation of capture in the metadata of a photo, in many instances this information is unavailable or out of date: seconds, hours or sometimes even months.”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
The embedding concept moves from dense word vector representations into a new domain, knowledge graph entities and relations for link prediction tasks.'.}}.— the tool’s reading
“Word embeddings represent words as dense unit vectors of real numbers, where vectors that are close together are semantically related.”
“Recent advances in knowledge-graph-based research focus on knowledge representation learning (KRL) or knowledge graph embedding (KGE) by mapping entities and relations into low-dimensional vectors while capturing their semantic meanings <cit.>.”
The newer paper (WaveNet) carries the general latent embedding conditioning idea from image generation (PixelCNN) into speech synthesis via speaker embeddings for global conditioning.— the tool’s reading
“For images, the equivalent of a causal convolution is a masked convolution (van den Oord et al., 2016a) which can be implemented by constructing a mask tensor and doing an elementwise multiplication of this mask with the convolution kernel before applying it.”
“Remarkably, these architectures are able to model distributions over thousands of random variables (e.g. 64×64 pixels as in PixelRNN (van den Oord et al., 2016a)).”
“Similarly to PixelCNNs (van den Oord et al., 2016a;b), the conditional probability distribution is modelled by a stack of convolutional layers.”
“The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks.”
“Global conditioning is characterised by a single latent representation vech that influences the output distribution across all timesteps, e.g.a speaker embedding in a TTS model.”
The newer paper restricts the general latent-representation notion of embedding to a specific L2-normalized ConvNet output used for cluster-prototype comparison in self-supervised image tasks.confidenceLimits.— the tool’s reading
“Many self-supervised methods manipulate the input data to extract a supervised signal in the form of a pretext task [1, 13, 26, 28, 30, 36, 38, 41, 42, 47, 48, 57].”
“This is similar in spirit to word2vec <cit.> which learns word representation from natural language sentences by predicting a word given its context.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
The newer paper generalizes the earlier context-encoder's latent embedding into a formal contrastive dictionary look-up framework with normalized query/key vectors and dot-product similarity.— the tool’s reading
“, denoising auto-encoders [58], context autoencoders [48], or cross-channel auto-encoders (colorization) [64, 65].”
“The pretext task in contrastive predictive coding (CPC) [46] is a form of context auto-encoding [48], and in contrastive multiview coding (CMC) [56] it is related to colorization [64].”
“This is similar in spirit to word2vec <cit.> which learns word representation from natural language sentences by predicting a word given its context.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
ELMo generalizes static pretrained embeddings used in ESIM by making them context-dependent, while still being used as input features to the same architecture, extending the earlier fixed-vector approach.— the tool’s reading
“Our baseline, the ESIM sequence model from Chen et al. (2017), uses a biL-STM to encode the premise and hypothesis, followed by a matrix attention layer, a local inference layer, another biLSTM inference composition layer, and finally a pooling operation before the output layer.”
“…pretrained word vectors (Turian et al., 2010; Mikolov et al., 2013; Pennington et al., 2014) are a standard component of most state-of-the-art NLP architectures, including for question answering (Liu et al., 2017), textual entailment (Chen et al., 2017) and semantic role labeling (He et al., 2017).”
“Overall, adding ELMo to the ESIM model improved accuracy by 0.7% establishing a new single model state-of-the-art of 88.7%, and a five member ensemble pushes the overall accuracy to 89.3%.”
“The 𝐚_i or 𝐛_j ∈ℝ^l is an embedding of l-dimensional vector, which can be initialized with some pre-trained word embeddings and organized with parse trees.”
“Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence.”
The earlier word-vector embedding concept is carried into a new domain—general latent representation learning via nonlinear encoders for arbitrary signals, not just words.ed.— the tool’s reading
“Although we cannot evaluate p ( x ) or p ( x | c ) directly, we can use samples from these distributions, allowing us to use techniques such as Noise-Contrastive Estimation [12, 14, 15] and Importance Sampling [16] that are based on comparing the target value with randomly sampled negative values.”
“The logit is generally computed as an inner product z_w = h^Te_w where h is a context vector and e_w is a “word embedding” for w.”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
The newer paper reverts to standard per-token lookup embeddings, dropping the earlier dynamic character-CNN generation approach to a simpler, more restricted embedding form.— the tool’s reading
“The logit is generally computed as an inner product z_w = h^Te_w where h is a context vector and e_w is a “word embedding” for w.”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
The vector-embedding concept from word representations is carried into speech synthesis as a speaker/style embedding for global conditioning, a new modality/domain.ergy.png— the tool’s reading
“The logit is generally computed as an inner product z_w = h^Te_w where h is a context vector and e_w is a “word embedding” for w.”
“Global conditioning is characterised by a single latent representation vech that influences the output distribution across all timesteps, e.g.a speaker embedding in a TTS model.”
The newer paper generalizes simple word-vector lookup embeddings into a composite multimodal embedding summing token, visual, segment, and position components.'— the tool’s reading
“such as embedding lookup and attention module, remain on the CPU, and all other quantized operations are off-loaded to the TPU.”
“For each input element, its embedding feature is the summation of four types of embedding, namely, token embedding, visual feature embedding, segment embedding, and sequence position embedding.”
The newer paper builds on wordpiece embedding lookups by adding contextual, dense representations (BERT) that generalize static embeddings for semantic similarity tasks.— the tool’s reading
“The BERT model tokenizes the input text into a sequence of word pieces (Wu et al., 2016), where unknown words are split into several commonly observed sequences of characters.”
“such as embedding lookup and attention module, remain on the CPU, and all other quantized operations are off-loaded to the TPU.”
“In contrast to prior word embeddings <cit.>, contextual embeddings, such as BERT <cit.> and ELMo <cit.>, can generate different vector representations for the same word in different sentences depending on the surrounding words, which form the context of the target word.”
The embedding concept moves from word-to-vector lookup in NLP translation to a graph-level fixed-length representation in graph neural networks, a new domain and modality.'.,— the tool’s reading
“The past decade has seen remarkable success in the use of deep neural networks to understand and translate natural language (Wu et al., 2016), generate and decode complex audio signals (Hinton et al., 2012), and infer features from real-world images and videos (Krizhevsky et al., 2012).”
“such as embedding lookup and attention module, remain on the CPU, and all other quantized operations are off-loaded to the TPU.”
“Then, after M steps of computation, the set2set model produces a graph level embedding q_t^* which is invariant to the order of the of the tuples T.”
Both use embedding as token-to-vector lookup, but the newer paper narrows citation to word-piece vocabulary details rather than extending the embedding concept itself.— the tool’s reading
“For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [31].”
“This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [31, 2, 8].”
“In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [31] and byte-pair [25] representations.”
“such as embedding lookup and attention module, remain on the CPU, and all other quantized operations are off-loaded to the TPU.”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
The newer paper restricts the general CNN embedding concept to a specific end-to-end trained VLAD-pooled vector optimized solely for place/image retrieval via distance metric learning.— the tool’s reading
“We extract image embeddings from the final layer below the SoftMax layer (a 2048-dim. vector) and rank images by the Euclidean distance between their embedding vectors.”
“This is generally done by designing a function f which acts as the “image representation extractor”, such that given an image I_i it produces a fixed size vector f(I_i).”
The newer paper restricts the general Hilbert-space embedding to a specific single-qubit Bloch-sphere encoding via rotations for class separability, a special case of the earlier general embedding.— the tool’s reading
“For quantum principal component analysis of classical data (qPCA <cit.>), we choose a data vector v⃗_j at random, and use a quantum random access memory (qRAM <cit.>) to map that vector into a quantum state: v⃗_j → |v_j⟩.”
“However, if more layers are applied, each one will perform a different rotation, and many different rotations together have the capability of enabling a feature map. Data embedded in this feature space can be easily separated into classes employing the regions on the Bloch sphere.”
The newer paper carries node/graph embedding techniques like GCN encoders into the knowledge graph link prediction domain, adapting them to entity/relation representation.confidence reflects some overlap with extension.}— the tool’s reading
“Here, the GCN [60] acts as a graph encoder.”
“Right: t-SNE <cit.> visualization of hidden layer activations of a two-layer GCN trained on the Cora dataset <cit.> using 5% of labels.”
“Recent advances in knowledge-graph-based research focus on knowledge representation learning (KRL) or knowledge graph embedding (KGE) by mapping entities and relations into low-dimensional vectors while capturing their semantic meanings <cit.>.”
The newer paper carries GCN-based node embedding techniques from general graph representation learning into the specific domain of user-item latent factors for recommendation systems.— the tool’s reading
“Later on, GraphSage [14] and GCN [23] re-define graph convolution in the spatial domain, i.”
“of standard GCN [23], which can avoid the scale of embeddings increasing with graph convolution operations; other choices can also be applied here, such as the L1 norm, while empirically we find this symmetric normalization has good performance (see experiment results in Section 4.”
“It takes inspiration from the Graph Convolution Network (GCN) [14, 23], following the same ar X iv :2 00 2.”
“Right: t-SNE <cit.> visualization of hidden layer activations of a two-layer GCN trained on the Cora dataset <cit.> using 5% of labels.”
“The most common paradigm for CF is to learn latent features (a.k.a. embedding) to represent a user and an item, and perform prediction based on the embedding vectors <cit.>.”
The GCN-based node embedding idea is carried into a new domain—collaborative knowledge-graph recommendation—applying it to users, items, and entities with attention.— the tool’s reading
“Towards this end, we take inspiration from the recent developments of graph neural networks [9, 17, 28], which have the potential of achieving the goal but have not been explored much for KG-based recommendation.”
“• GC-MC [26]: Such model is designed to employ GCN [17] encoder on graph-structured data, especially for the user-item bipartite graph.”
“Next we build upon the architecture of graph convolution network [17] to recursively propagate embeddings along highorder connectivity; moreover, by exploiting the idea of graph attention network [28], we generate attentive weights of cascaded propagations to reveal the importance of such connectivity.”
“Right: t-SNE <cit.> visualization of hidden layer activations of a two-layer GCN trained on the Cora dataset <cit.> using 5% of labels.”
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
The newer paper generalizes node-level embeddings into a permutation-invariant graph-level readout, building on GCN-style message passing as a special case within its MPNN framework.— the tool’s reading
“The Kipf & Welling (2016) model results in a message function Mt(htv, h t w) = cvwh t w where cvw = (deg(v)deg(w))−1/2Avw.”
“Another family of models defined in Defferrard et al. (2016), Bruna et al. (2013), Kipf & Welling (2016) can be interpreted as MPNNs.”
“THE SPECIAL CASE OF KIPF AND WELLING (2016) Motivated as a first order approximation of the graph laplacian methods, Kipf & Welling (2016) propose the following layer-wise propagation rule: H l+1 = σ ( D̃−1/2ÃD̃−1/2H lW l ) (7) Here à = A + IN where A is the real valued adjacency matrix for an…”
“Right: t-SNE <cit.> visualization of hidden layer activations of a two-layer GCN trained on the Cora dataset <cit.> using 5% of labels.”
“Then, after M steps of computation, the set2set model produces a graph level embedding q_t^* which is invariant to the order of the of the tuples T.”
The newer paper carries GCN-style node embedding propagation from general graph learning into the recommendation domain, applying it to user-item latent factor vectors.— the tool’s reading
“GC-MC [29] applies the graph convolution network (GCN) [18] on user-item graph, however it only employs one convolutional layer to exploit the direct connections between users and items.”
“Following the graph convolutional network [18], we set pui as the graph Laplacian norm 1/ √ |Nu | |Ni |, where Nu and Ni denote the first-hop neighbors of user u and item i .”
“• GC-MC [29]: This model adopts GCN [18] encoder to generate the representations for users and items, where only the first-order neighbors are considered.”
“Right: t-SNE <cit.> visualization of hidden layer activations of a two-layer GCN trained on the Cora dataset <cit.> using 5% of labels.”
“Generally speaking, there are two key components in learnable CF models — 1) embedding, which transforms users and items to vectorized representations, and 2) interaction modeling, which reconstructs historical interactions based on the embeddings.”
The newer paper specializes generic node embeddings on graphs to entity/relation embeddings within knowledge graphs for the specific task of link prediction.'— the tool’s reading
“Relations in these datasets need not necessarily encode directed subject-object relations, but are also used to encode the presence, or absence, of a specific feature for a given entity.”
“†Canadian Institute for Advanced Research Academy is marked as a university).”
“Predicting missing information in knowledge bases is the main focus of statistical relational learning (SRL).”
“Right: t-SNE <cit.> visualization of hidden layer activations of a two-layer GCN trained on the Cora dataset <cit.> using 5% of labels.”
“The encoder maps each entity v_i ∈𝒱 to a real-valued vector e_i ∈ℝ^d.”
The newer paper generalizes node embeddings from plain graphs to knowledge graphs, adding relation-specific encoders/decoders for link prediction beyond adjacency reconstruction.— the tool’s reading
“2Note that this represents a simplification of the message passing neural network proposed in (Gilmer et al. 2017) that suffices to include the aforementioned models as special cases.”
“We demonstrate the ability of the VGAE and GAE models to learn meaningful latent embeddings on a link prediction task on several popular citation network datastets <cit.>.”
“The encoder maps each entity v_i ∈𝒱 to a real-valued vector e_i ∈ℝ^d.”
The newer paper cites the graph embedding work only as an application example while shifting the term 'embedding' to a different, informal usage within VAE architectures across a broader modeling context.'— the tool’s reading
“Other applications include modeling of graphs (Kipf and Welling, 2016) (Variational Graph Autoencoders), learning of 3D structure from images (Rezende et al.”
“We demonstrate the ability of the VGAE and GAE models to learn meaningful latent embeddings on a link prediction task on several popular citation network datastets <cit.>.”
“Its learning algorithm is a mix of classical (amortized, variational) expectation maximization but through the reparameterization trick ends up backpropagating through the many layers of the deep neural networks embedded inside of it.”
The newer paper generalizes embedding from a specific speaker-conditioning vector to a broader compressed latent representation applicable across signal types and prediction tasks.'— the tool’s reading
“More recent advancements in autoregressive modeling such as masked convolutional architectures [18, 19] or self-attention networks [20] could help improve results further.”
“Global conditioning is characterised by a single latent representation vech that influences the output distribution across all timesteps, e.g.a speaker embedding in a TTS model.”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
The newer paper carries the general embedding concept from speech synthesis conditioning into graph/manifold representation learning, applying it to a different domain and modality.— the tool’s reading
“Global conditioning is characterised by a single latent representation vech that influences the output distribution across all timesteps, e.g.a speaker embedding in a TTS model.”
“However, a Riemannian manifold can be realized as a subset of a Euclidean space (in which case it is said to be embedded in that space) by using the structure of the Euclidean space to induce a Riemannian metric.”
The core embedding notion of fixed-size latent representation is carried from sentences (NLP) into nodes/graphs in network science, a new domain and modality.riangle.— the tool’s reading
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
“Network embedding (or graph embedding) has been widely used in many real-world applications.”
The newer paper adopts the earlier self-attentive BiLSTM sentence embedding as one encoder architecture, applying it more broadly to transferable universal sentence representations trained on NLI.— the tool’s reading
“Following Lin et al. (2017) we use a self-attentive network with multiple views of the input sentence, so that the model can learn which part of the sentence is important for the given task.”
“The self-attentive sentence encoder (Liu et al., 2016; Lin et al., 2017) uses an attention mechanism over the hidden states of a BiLSTM to generate a representation u of an input sentence.”
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
“Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.”
The newer paper reverts embedding to the basic token-to-vector lookup, a simpler special case compared to the earlier sentence-level attention-weighted matrix representation.confidence— the tool’s reading
“Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 22, 23, 19].”
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
The newer survey cites the sentence-embedding self-attention work only as NLP background while generalizing 'embedding' into a broader feature-transform concept used across attention modules, effectively shifting context.the term into a different domain/usage.=— the tool’s reading
“Transformers have had great success in natural language processing [33], [38], [149], [150], [152], [161].”
“Self-attention was proposed and has had great success in the field of natural language processing (NLP) [33], [38], [39], [149], [150], [151], [152].”
“Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.”
“where ϕ denotes an embedding function used for dimensionally reduction, ϕ(x)^T is the transpose of ϕ(x) along with the channel dimension, Utri(·) is the operator that extracts the upper triangular elements of a matrix and Vec(·) is vectorization.”
BERT builds on the Transformer's static learned embeddings, adding contextualization to produce context-dependent vector representations for semantic similarity tasks.atable— the tool’s reading
“BERT uses a Transformer encoder (Vaswani et al., 2017) trained on masked language modeling and next-sentence prediction tasks.”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
“In contrast to prior word embeddings <cit.>, contextual embeddings, such as BERT <cit.> and ELMo <cit.>, can generate different vector representations for the same word in different sentences depending on the surrounding words, which form the context of the target word.”
The generic dense-vector embedding concept from sequence modeling is carried into a new domain—knowledge-graph-based recommendation—adapted via neighbor propagation and attention.'— the tool’s reading
“Specifically, we propose a new method named Knowledge Graph Attention Network (KGAT), which is equipped with two designs to correspondingly address the challenges in high-order relation modeling: 1) recursive embedding propagation, which updates a node’s embedding based on the embeddings of its neighbors, and recursively performs such embedding propagation t…”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
The embedding concept moves from sequence modeling (token vectors) to recommendation systems (user-item latent vectors), adapting the idea to a new domain and data structure.— the tool’s reading
“In future, we will further improve NGCF by incorporating the attention mechanism [2, 34, 35] to learn variable weights for neighbors during embedding propagation and for the connectivities of different orders.”
“, eu ⊙ ei ), which makes messages being propagated dependent on the affinity between ei and eu and functions like the attention mechanism [2, 34].”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
“Generally speaking, there are two key components in learnable CF models — 1) embedding, which transforms users and items to vectorized representations, and 2) interaction modeling, which reconstructs historical interactions based on the embeddings.”
The newer paper builds on the Transformer's learned embedding mechanism, applying and generalizing it to cross-lingual word and sentence representations via pretraining.-— the tool’s reading
“In all experiments, we use a Transformer architecture with 1024 hidden units, 8 heads, GELU activations (Hendrycks and Gimpel, 2016), a dropout rate of 0.1 and learned positional embed-dings.”
“Our causal language modeling (CLM) task consists of a Transformer language model trained to model the probability of a word given the previous words in a sentence P ( w t | w 1 , . . . , w t − 1 , θ ) .”
“In this context, a Transformer [38] language model is learned on a large unsupervised text corpus, and then fine-tuned on natural language understanding (NLU) tasks such as classification [35] or natural language inference [7, 42].”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
“suggest that it is possible to build universal cross-lingual encoders that can encode any sentence into a shared embedding space.”
The newer paper transplants the dense vector embedding concept from sequence/token modeling into node/graph representation learning within network structures.'}— the tool’s reading
“in A GAT G º = »aijn n into Eq. 5. This completes the denition of a single-head graph attention. In addition, we apply multi-headgraphattentionassuggestedbyVelickovicetal .[49] and Vaswani et al . [48] . The multi-head attention mechanism performs K independent single attention in parallel, i.e., we have K independentparameters W 1; ;W K andattentionmatrix A…”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
“More formally, network embedding learns an embedding matrix X ∈RD×|V |, with each column corresponding to the representation of a vertex (user) in the network G. In the proposed model, we use a pre-trained embedding layer which maps a user u to her D-dimensional representation xu ∈RD, as shown in Figure 2(b).”
The newer paper carries the embedding concept from token-level NLP sequence modeling into speech synthesis for encoding speaker/style/prosody information, a new modality and domain.— the tool’s reading
“In our experiments, we found that using multi-head attention (Vaswani et al., 2017) significantly improves style transfer performance, and is more effective than simply increasing the number of tokens.”
“In our experiments, we found that using multi-head attention (Vaswani et al., 2017) significantly improves style transfer performance, and, moreover, is more effective than simply increasing the number of tokens.”
“Unlike in (Vaswani et al., 2017), we use MLP-based attention instead of dot-product attention for each attention head in our experiments.”
“Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d_model.”
“GST embeddings can also be viewed as an external memory that stores style information extracted from training data. The reference signal guides memory writes at training time, and memory reads at inference time.”
The newer paper restricts the general dense-vector embedding concept specifically to image encoder outputs used for curation and downstream visual tasks, a domain-specific case.'— the tool’s reading
“Our approach is inspired by text curation pipelines (Wenzek et al., 2020), where a language model is trained on Wikipedia to score texts extracted from an uncurated source.”
“Popular examples include the text representation known as word2vec <cit.>, representations of images by convolutional neural networks <cit.>, and image descriptors for instance search <cit.>. Such representations or embeddings are usually real-valued, high-dimensional vectors of 50 to 1000+ dimensions.”
“In order to do this, we first compute an image embedding using a self-supervised ViT-H/16 network pretrained on ImageNet-22k, and use cosine-similarity as a distance measure between images.”
The newer paper restricts the general dense-vector embedding notion to image-encoder outputs shaped by contrastive/prototype objectives, a specific domain and training scheme.— the tool’s reading
“For efficient clustering, we adopt the GPU k-means implementation in faiss [36].”
“Popular examples include the text representation known as word2vec <cit.>, representations of images by convolutional neural networks <cit.>, and image descriptors for instance search <cit.>. Such representations or embeddings are usually real-valued, high-dimensional vectors of 50 to 1000+ dimensions.”
“unsupervised visual representation learning aims to learn an embedding function f_θ (realized via a DNN) that maps X to V={v_1, v_2,...,v_n} with v_i=f_θ(x_i), such that v_i best describes x_i.”
The newer paper restricts the general speaker embedding concept to focus specifically on improving the pooling step for verification tasks, a narrower special-case treatment.— the tool’s reading
“, a Time-Delay Neural Network (TDNN) [9], Convolutional Neural Network (CNN) [7, 8], LSTM [10, 11], or Gated Recurrent Unit (GRU) [8].”
“In the “Embedding” column, average [7, 8] denotes average pooling that used only means, attention [10, 11] used weighted means scaled by attention (attentive average pooling), statistics [9] used both means and standard deviations (statistics pooling), and attentive statistics is the proposed method (attentive statistics pooling), which used both weighted me…”
“Some studies refrain from using softmax layers and achieve end-to-end neural networks by using contrastive loss [7] or triplet loss [8].”
“We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering.”
“an average pooling layer has been introduced to aggregate frame-level speaker feature vectors to obtain an utterance-level feature vector, i.e., speaker embedding, with a fixed number of dimensions.”
The newer paper reuses the generic learned-embedding notion for softmax token probabilities, a more general/basic usage than K-NRM's task-specific similarity-tuned embeddings, effectively restricting the concept to standard token representation.— the tool’s reading
“We use a pretrained XLNet to extract word embeddings for the documents and queries without finetuning, and employ a kernel pooling network [36] to rank the documents.”
“first uses an to map each word t to an L-dimension embedding v⃗_t:”
“and e(x) denotes the embedding of x.”
The newer paper carries GraphSAGE's neighborhood-aggregation embedding idea into recommendation systems, applying it to user-item interaction graphs as latent factor vectors.'— the tool’s reading
“Later on, GraphSage [14] and GCN [23] re-define graph convolution in the spatial domain, i.”
“Many work have specified the AGG, such as the weighted sum aggregator in GIN [42], LSTM aggregator in GraphSAGE [14], and bilinear interaction aggregator in BGNN [48] etc.”
“It takes inspiration from the Graph Convolution Network (GCN) [14, 23], following the same ar X iv :2 00 2.”
“The basic idea behind node embedding approaches is to use dimensionality reduction techniques to distill the high-dimensional information about a node's graph neighborhood into a dense vector embedding.”
“The most common paradigm for CF is to learn latent features (a.k.a. embedding) to represent a user and an item, and perform prediction based on the embedding vectors <cit.>.”
KGAT builds on GraphSAGE's neighborhood aggregation for node embeddings, adding attention-weighted propagation over knowledge graphs to model user-item-entity relations.— the tool’s reading
“lop a model that can exploit high-order information in KG in an efficient, explicit, and end-to-end manner. Towards this end, we take inspiration from the recent developments of graph neural networks [9, 17, 28], which have the potential of achieving the goal but have not been explored much for KG-based recommendation. Specifically, we propose a new method n…”
“forward, the attention flow suggests parts of the data to focus on, which can be treated as explanations behind the recommendation. Distinct from the information propagation in GCN [17] and GraphSage [9] which set the discount factor between two nodes as 1/ p |Nh||N t |or , our model not only exploits the proximity structureofgraph,butalsospecifyvaryingimpor…”
“we set the activation function set as LeakyReLU [20]; W ∈Rd′×d are the trainable weight matrices to distill useful information for propagation, and d′is the transformation size. •GraphSage Aggregator [9] concatenates two representations, followed by a nonlinear transformation: fGraphSage = LeakyReLU W(eh||eN h ) , (7) where ||is the concatenation operation…”
“The basic idea behind node embedding approaches is to use dimensionality reduction techniques to distill the high-dimensional information about a node's graph neighborhood into a dense vector embedding.”
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
The GNN-based node embedding aggregation idea (GraphSAGE) is carried into the recommendation domain to propagate collaborative signal for user-item latent vectors.— the tool’s reading
“This is inspired by the recent developments of graph neural networks [8, 32, 38], which can be seen as constructing information flows in the embedding space.”
“Next we build upon the message-passing architecture of GNNs [8, 38] in order to capture CF signal along the graph structure and refine the embeddings of users and items.”
“• PinSage [42]: PinSage is designed to employ GraphSAGE [8] on item-item graph.”
“The basic idea behind node embedding approaches is to use dimensionality reduction techniques to distill the high-dimensional information about a node's graph neighborhood into a dense vector embedding.”
“Generally speaking, there are two key components in learnable CF models — 1) embedding, which transforms users and items to vectorized representations, and 2) interaction modeling, which reconstructs historical interactions based on the embeddings.”
The newer paper applies node embedding concepts specifically to knowledge graphs with relations, restricting general graph embeddings to entity/relation representations for link prediction.— the tool’s reading
“The basic idea behind node embedding approaches is to use dimensionality reduction techniques to distill the high-dimensional information about a node's graph neighborhood into a dense vector embedding.”
“The encoder maps each entity v_i ∈𝒱 to a real-valued vector e_i ∈ℝ^d.”
The newer paper shifts embedding-space reasoning from abstract distribution-matching contexts to neural network activation spaces for interpretability, applying the concept in a new domain rather than extending or contesting it directly.— the tool’s reading
“In view of the multiple challenges posed by analyzing deep neural network functions, building robust and practical methods to explain their decisions has developed into an own research area [100], [48], [123] and an abundance of methods have been proposed.”
“A more relevant information to the user would be the overall contribution of a subgroup of features I on a group of data points G (cf. [82], [100]).”
“Orthogonal to the quest for better and more holistic machine learning models, interpretable ML [123], [56], [90], [100], [162], [48], [16], [12] has developed as a subfield of machine learning that seeks to augment the training process, the learned representations and the decisions with…”
“Examples of domains that are not interpretable are abstract vector spaces (e.g. word embeddings <cit.>), or domains composed of undocumented input features (e.g. sequences with unknown words or symbols).”
“Further methods do not seek to explain in terms of input features, but in terms of the latent space, where the directions in the latent space code for higher-level concepts, such as color, material, object part, or object <cit.>.”
The newer paper generalizes 'embedding' from face-specific FaceNet vectors to a broader relation-based knowledge transfer space, though citation context only discusses MobileNet architecture, not embeddings directly.'— the tool’s reading
“Recently, depth-wise separable convolution has been widely used to design efficient neural networks for mobile or embedded devices (Chollet 2017; Howard et al. 2017; Sandler et al. 2018; Zhang et al. 2018a; Ma et al. 2018).”
“including depthwise separable convolution, as inMobileNets (Howard et al. 2017; Sandler et al. 2018) and ShuffleNets (Zhang et al.”
“Recently, depth-wise separable convolution has been widely used to design efficient neural networks for mobile or embedded devices (Chollet 2017; Howard et al. 2017; San-dler et al. 2018; Zhang et al. 2018a; Ma et al. 2018).”
“It builds face embeddings based on the triplet loss.”
“Based on idea of manifold learning, the student network is learned by feature embedding, which preserves the feature similarities of samples in the intermediate layers of the teacher networks <cit.>.”
The newer paper repurposes the embedding concept from general graph-level message passing representations into entity/relation embeddings for knowledge graph link prediction, a new application domain.— the tool’s reading
“R-GCNs can further be seen as a sub-class of message passing neural networks (Gilmer et al. 2017), which encompass a number of previous neural models for graphs, including GCNs, under a differentiable message passing interpretation.”
“(2)Note that this represents a simplification of the message passing neural network proposed in (Gilmer et al. 2017) that suffices to include the aforementioned models as special cases.”
“2009) can be understood as special cases of a simple differentiable message-passing framework (Gilmer et al. 2017):”
“Then, after M steps of computation, the set2set model produces a graph level embedding q_t^* which is invariant to the order of the of the tuples T.”
“The encoder maps each entity v_i ∈𝒱 to a real-valued vector e_i ∈ℝ^d.”
The vector-embedding idea moves from matrix-factorization latent factors in recommendation into knowledge-graph entity/relation embeddings for link prediction, a new representational domain.of translation-based scoring.trimmed— the tool’s reading
“Content-based methods deal with the issues by introducing various side information, such as the contextual reviews [9, 25], relational data [10, 36] and knowledge graphs [6].”
“MF maps users and items into a latent factor space and represents users' preferences and items' features by f-dim latent factor vectors (i.e., p_u∈ℝ^f × 1 and q_i∈ℝ^f × 1).”
“Its basic idea is to learn embeddings for entities and relations, satisfying 𝐞_h+𝐫≈𝐞_t if there is a triplet (e_h, e_t, r) in KG.”
The newer paper reduces embedding to a simple static learned token vector for softmax output, dropping ELMo's context-dependent, layer-combination formulation.— the tool’s reading
“Unsupervised representation learning has been highly successful in the domain of natural language processing [7, 22, 27, 28, 10].”
“AR language modeling seeks to estimate the probability distribution of a text corpus with an autoregressive model [7, 27, 28].”
“Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence.”
“and e(x) denotes the embedding of x.”
The newer paper reduces ELMo's context-dependent, multi-layer representation to a generic static 'representation function' learned via unsupervised prediction, ignoring its contextual nuance.— the tool’s reading
“In natural language processing (NLP), low-dimensional representations of text – called text embeddings – have been computed with unlabeled data (Peters et al., 2018; Devlin et al., 2018).”
“Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence.”
“In natural language processing (NLP), low-dimensional representations of text – called text embeddings – have been computed with unlabeled data <cit.>. Often the embedding function is trained by using the embedding of a piece of text to predict the surrounding text <cit.>.”
The newer paper carries the embedding concept from context-dependent NLP word/token representations into a contrastive representation-learning framework over convolutional image features for mutual information estimation.ed— the tool’s reading
“e wide-reaching benefits. For example, consider the ubiquitous use of pre-trained model components, such as word vectors [Mikolov et al., 2013, Pennington et al., 2014] and context-sensitive encoders [Peters et al., 2018, Devlin et al., 2019], for achieving state-of-the-art results on hard NLP tasks. Similarly, large convolutional networks pre-trained on larg…”
“For example, consider the ubiquitous use of pre-trained model components, such as word vectors [Mikolov et al., 2013, Pennington et al., 2014] and context-sensitive encoders [Peters et al., 2018, Devlin et al., 2019], for achieving state-of-the-art results on hard NLP tasks.”
“Our representations differ from traditional word type embeddings in that each token is assigned a representation that is a function of the entire input sentence.”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
The newer paper carries the embedding concept from generic network embedding into the recommendation domain, redefining it as trainable user-item latent vectors refined via graph convolution.02— the tool’s reading
“Owing to its interpretability and efficiency, it quickly becomes a prevalent formulation of GNNs and is being widely used [11, 29, 47].”
“More formally, network embedding learns an embedding matrix X ∈RD×|V |, with each column corresponding to the representation of a vertex (user) in the network G. In the proposed model, we use a pre-trained embedding layer which maps a user u to her D-dimensional representation xu ∈RD, as shown in Figure 2(b).”
“The most common paradigm for CF is to learn latent features (a.k.a. embedding) to represent a user and an item, and perform prediction based on the embedding vectors <cit.>.”
The embedding idea moves from generic network/graph embedding to knowledge-graph-augmented recommendation, adding attention-based propagation for a new application domain.— the tool’s reading
“Considering an entity h, we use Nh = {(h, r , t )|(h, r , t ) ∈ G} to denote the set of triplets where h is the head entity, termed egonetwork [21].”
“More formally, network embedding learns an embedding matrix X ∈RD×|V |, with each column corresponding to the representation of a vertex (user) in the network G. In the proposed model, we use a pre-trained embedding layer which maps a user u to her D-dimensional representation xu ∈RD, as shown in Figure 2(b).”
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
The newer paper transfers the graph-embedding idea from generic network structure learning into the recommendation domain, redefining embeddings as user-item latent factors enriched via collaborative signal propagation.》— the tool’s reading
“It allows us to discard the node sampling procedure, which is commonly used to make graph convolution network runnable on large-scale graph [25].”
“More formally, network embedding learns an embedding matrix X ∈RD×|V |, with each column corresponding to the representation of a vertex (user) in the network G. In the proposed model, we use a pre-trained embedding layer which maps a user u to her D-dimensional representation xu ∈RD, as shown in Figure 2(b).”
“Generally speaking, there are two key components in learnable CF models — 1) embedding, which transforms users and items to vectorized representations, and 2) interaction modeling, which reconstructs historical interactions based on the embeddings.”
PinSage's GCN-based node embedding for pin/board recommendation is carried into the user-item bipartite CF graph, adapting graph convolution to a new domain and embedding role.— the tool’s reading
“The main competing method is NGCF, which has shown to outperform several methods including GCNbased models GC-MC [35] and PinSage [45], neural network-based models NeuMF [19] and CMN [10], and factorization-based models MF [32] and HOP-Rec [43].”
“Motivated by the strength of graph convolution, recent efforts like NGCF [39], GC-MC [35], and PinSage [45] adapt GCN to the user-item interaction graph, capturing CF signals in high-hop neighbors for recommendation.”
“Collaborative Filtering (CF) is a prevalent technique in modern recommender systems [7, 45].”
“We develop a data-efficient Graph Convolutional Network (GCN) algorithm RW-GCN, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information.”
“The most common paradigm for CF is to learn latent features (a.k.a. embedding) to represent a user and an item, and perform prediction based on the embedding vectors <cit.>.”
NGCF generalizes GCN-based embedding propagation from PinSage's item-item graph to user-item bipartite graphs, adding higher-order collaborative signal and interaction terms.— the tool’s reading
“raph (cf. Equation (3)), we make NGCF effective in exploiting the CF signal in high-order connectivities. Here we discuss existing recommendation methods that also employ graph convolution operations [29, 42, 43]. GC-MC [29] applies the graph convolution network (GCN) [18] on user-item graph, however it only employs one convolutional layer to exploit the dir…”
“the same items. •HOP-Rec [40]: This is a state-of-the-art graph-based model, where the high-order neighbors derived from random walks are exploited to enrich the user-item interaction data. •PinSage [42]: PinSage is designed to employ GraphSAGE [8] on item-item graph. In this work, we apply it on user-item 3The previous implementation of ndcg metric in NGCF …”
“0.0261∗ %Improv. 11.68% 8.64% 11.97% 11.29% 9.61% 12.50% p-value 2.01e-7 3.03e-3 5.34e-3 4.62e-4 3.48e-5 1.26e-4 interaction graph. Especially, we employ two graph convolution layers as suggested in [42], and the hidden dimension is set equal to the embedding size. •GC-MC [29]: This model adopts GCN [18] encoder to generate the representations for users and …”
“We develop a data-efficient Graph Convolutional Network (GCN) algorithm RW-GCN, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information.”
“Generally speaking, there are two key components in learnable CF models — 1) embedding, which transforms users and items to vectorized representations, and 2) interaction modeling, which reconstructs historical interactions based on the embeddings.”
KGAT builds on TransE-based embeddings (CFKG) by adding recursive neighbor propagation and attention to capture high-order connectivity, generalizing the earlier translation-based embedding approach.二— the tool’s reading
“As we can see, KGAT achieves comparable computation complexity to SL models (FM and NFM) and regularization-based methods (CFKG and CKE), being much efficient that path-based methods (MCRec and RippleNet).”
“Empirically, FM, NFM, CFKG, CKE, GC-MC, KGAT , MCRec, and RippleNet cost around 700s, 780s, 800s, 420s, 500s, 560 s, 20 hours, and 2 hours for all testing instances on Amazon-Book dataset, respectively.”
“• CFKG [1]: The model applies TransE [2] on the unified graph including users, items, entities, and relations, casting the recommendation task as the plausibility prediction of ( u , Interact , i ) triplets.”
“Inspired by <cit.>, we project each entity and relation into a unified low-dimensional embedding space. Intuitively, the embedding of a tail entity should be close to its translated head entity embedding.”
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
The newer paper restricts the general latent-embedding notion to L2-normalized ConvNet outputs for augmented image views compared via prototypes, a specific self-supervised image domain application.rn— the tool’s reading
“They rely on noise contrastive estimation [18] to compare instances, which is a special form of contrastive learning [24, 40].”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
The newer paper builds on the earlier encoder-derived latent embedding concept, adding normalization and dictionary-lookup contrastive framing for representation learning.— the tool’s reading
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
The newer paper builds on the earlier encoder-derived latent embedding and InfoNCE loss, applying it within a broader contrastive framework with momentum-updated negatives and queries.'— the tool’s reading
“1a) [13, 8, 17, 1, 9, 2], the negative keys are from the same batch and updated end-to-end by back-propagation.”
“Recent studies on unsupervised representation learning from images [16, 13, 8, 17, 1, 9, 15, 6, 12, 2] are converging on a central concept known as contrastive learning [5].”
“An ef-encoder fective contrastive loss function, called InfoNCE [13], is: Here q is a query representation, k + is a representation of the positive (similar) key sample, and { k − } are representations of the negative (dissimilar) key samples. τ is a temperature hyper-parameter.”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
The newer paper restricts the general latent embedding concept to a specific case: encoder-projection output vectors for augmented image views compared via similarity, without predictive/mutual-information framing.— the tool’s reading
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
The newer paper restricts the general encoder-derived embedding concept to a specific self-supervised image-representation setting with projector outputs compared via cross-correlation, replacing the contrastive InfoNCE mechanism.— the tool’s reading
“For example, the redundancy reduction term plays a role similar to the contrastive term in the INFONCE objective (Oord et al., 2018), as discussed in detail in Section 5.”
“The I NFO NCE loss, where NCE stands for Noise-Contrastive Estimation (Gutmann & Hyv¨arinen, 2010), is a popular type of contrastive loss function used for self-supervised learning (e.g. (Chen et al., 2020a; He et al., 2019; H´enaff et al., 2019; Oord et al., 2018)).”
“For example, the redundancy reduction term plays a role similar to the contrastive term in the INFO NCE objective (Oord et al., 2018), as discussed in detail in Section 5.”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
“We call the output of the encoder the 'representations' and the output of the projector the 'embeddings'. The representations are used for downstream tasks and the embeddings are fed to the loss function of Barlow Twins.”
The newer paper specializes the general learned embedding into a specific L2-normalized, temperature-scaled vector form tailored for contrastive loss computation, a special case of the earlier general representation concept.— the tool’s reading
“• CPC v1 and v2 (Oord et al., 2018; Hénaff et al., 2019) define the context prediction task using a deterministic strategy to split examples into patches, and a context aggregation network (a PixelCNN) to aggregate these patches.”
“Discriminative approaches based on contrastive learning in the latent space have recently shown great promise, achieving state-of-the-art results (Hadsell et al., 2006; Dosovitskiy et al., 2014; Oord et al., 2018; Bachman et al., 2019).”
“Discriminative approaches based on contrastive learning in the latent space have recently shown great promise, achieving state-of-theart results (Hadsell et al., 2006; Dosovitskiy et al., 2014; Oord et al., 2018; Bachman et al., 2019).”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper builds on CPC's contrastive MI bound and embedding idea, but generalizes it into explicitly parameterized similarity-space projection functions for broader representation learning.— the tool’s reading
“Alternatively, one could generate multiple views by repeatedly partitioning the image’s pixels into “past” and “future” sets, with the considered partitions corresponding to a fixed autoregressive ordering, as in Contrastive Predictive Coding [CPC, van den Oord et al., 2018].”
“While the mutual information bounds in [Vondrick et al., 2018, Eslami et al., 2018] rely on explicit density estimation, our method uses the contrastive bound from CPC [van den Oord et al., 2018], which has been further analyzed by McAllester and Stratos [2018], and Poole et al. [2019].”
“→ The methods [1, 2, 3, 4, 5] are from: [Gidaris et al., 2018, Dosovitskiy et al., 2014, Doersch and Zisserman, 2017, Noroozi and Favaro, 2016, van den Oord et al., 2018].”
“In this paper we propose the following: first, we compress high-dimensional data into a much more compact latent embedding space in which conditional predictions are easier to model.”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
KGAT generalizes embedding learning by adding recursive neighbor propagation and attention over the collaborative knowledge graph, building on prior entity/relation embedding methods like RippleNet.— the tool’s reading
“ion task as the plausibility prediction of (u ,Interact i) triplets. •MCRec[14]: This is a path-based model, which extracts qualified meta-paths as connectivity between a user and an item. •RippleNet [29]: Such model combines regularization- and pathbased methods, which enrich user representations by adding that of items within paths rooted at each user. •GC…”
“paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM [11] and RippleNet [29]. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention m…”
“res the model to carefully weight (or select) them. Several recent efforts have attempted to leverage the CKG structure for recommendation, which can be roughly categorized into two types, path-based [14, 25, 29, 33, 37, 39] and regularizationbased [5, 15, 33, 38]: •Path-based methods extract paths that carry the high-order information and feed them into pre…”
“KGE intends to embed entities and relations in a KG into continuous vector spaces while preserving its inherent structure.”
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
The newer paper applies UMAP's embedding/dimensionality-reduction technique to a new domain—dense NLP document embeddings—rather than extending or contesting its original definition.'— the tool’s reading
“Thus, we use UMAP to reduce the dimensionality of document embeddings generated in 3.1 (McInnes et al., 2018).”
“and global features of high-dimensional data in lower projected dimensions (McInnes et al., 2018).”
“Although PCA and t-SNE are well-known methods for reducing dimensionality, UMAP has shown to preservers more of the local and global features of high-dimensional data in lower projected dimensions (McInnes et al., 2018).”
“we can use a spectral layout to initialize the embedding.”
“In BERTopic, we embed documents to create representations in vector space that can be compared semantically. We assume that documents containing the same topic are semantically similar.”
Both treat embeddings as encoder outputs capturing semantic similarity, but the newer paper restricts this to L2-normalized ConvNet outputs used specifically for prototype-based cluster assignment.}— the tool’s reading
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
The newer paper generalizes the embedding concept into a formal dictionary look-up/contrastive framework with normalized query-key comparisons, building on the earlier learned dense representation idea.— the tool’s reading
“The pretext task in contrastive predictive coding (CPC) [46] is a form of context auto-encoding [48], and in contrastive multiview coding (CMC) [56] it is related to colorization [64].”
“The networks fq and fk can be identical [29, 59, 63], partially shared [46, 36, 2], or different [56].”
“For this classifier, we perform a grid search and find the optimal initial learning rate is 30 and weight decay is 0 (similarly reported in [56]).”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
Both describe encoder-produced dense vectors compared via similarity metrics for contrastive-style learning, with newer paper relabeling 'embedding' as 'representation' in query/key terminology.'— the tool’s reading
“s. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public. 1. Introduction Recent studies on unsupervised representation learning from images [16,13,8,17,1,9,15,6,12,2] are converging on a central concept known as contrastive learning [5]. The results are promising: e.g., Momentum Contrast (MoCo) [6] …”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
Both use embedding as encoder output vector for similarity comparison, but the newer paper narrows it specifically to self-supervised image augmentation views via Siamese networks without labels.— the tool’s reading
“contrastive learning [16] is to attract the positive sample pairs and repulse the negative sample pairs. This methodology has been recently popularized for un-/self-supervised representation learning [36,30,20,37,21,2,35,17,29,8,9]. Simple and effective instantiations of contrastive learning have been developed using Siamese networks [37,2,17,8,9]. In practi…”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
SimCLR builds on the earlier embedding notion by adding L2-normalization and a projection head, refining the representation for contrastive loss without changing its core purpose.— the tool’s reading
“Table 6 compares our results with previous approaches (Zhuang et al., 2019; He et al., 2019a; Misra & van der Maaten, 2019; Hénaff et al., 2019; Kolesnikov et al., 2019; Donahue & Simonyan, 2019; Bachman et al., 2019; Tian et al., 2019) in the linear evaluation setting.”
“Not only does SimCLR outperform previous work (Figure 1), but it is also simpler, requiring neither specialized architectures (Bachman et al., 2019; Hénaff et al., 2019) nor a memory bank (Wu et al., 2018; Tian et al., 2019; He et al., 2019; Misra & van der Maaten, 2019).”
“(2018) proposes to use a memory bank to store the instance class representation vector, an approach adopted and extended in several recent papers (Zhuang et al., 2019; Tian et al., 2019; He et al., 2019a; Misra & van der Maaten, 2019).”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper generalizes the earlier dense embedding concept into multiple explicit, parameterized projection functions tied to NCE-based similarity scoring, building on the same core idea.dhcp— the tool’s reading
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
The embedding concept moves from cross-lingual word/sentence representations to multimodal vision-language transformer input features combining visual, positional, and segment cues.— the tool’s reading
“After that, a serious of approaches are proposed for pre-training the generic representation, mainly based on Transformers, such as GPT (Radford et al., 2018), BERT (Devlin et al., 2018), GPT-2 (Radford et al., 2019), XLNet (Yang et al., 2019), XLM (Lample & Conneau, 2019), and RoBERTa (Liu et al., 2019).”
“…a serious of approaches are proposed for pre-training the generic representation, mainly based on Transformers, such as GPT (Radford et al., 2018), BERT (Devlin et al., 2018), GPT-2 (Radford et al., 2019), XLNet (Yang et al., 2019), XLM (Lample & Conneau, 2019), and RoBERTa (Liu et al., 2019).”
“suggest that it is possible to build universal cross-lingual encoders that can encode any sentence into a shared embedding space.”
“For each input element, its embedding feature is the summation of four types of embedding, namely, token embedding, visual feature embedding, segment embedding, and sequence position embedding.”
The newer paper restricts embeddings to L2-normalized outputs compared against prototypes for clustering, a specific mechanism within the broader self-supervised representation concept.— the tool’s reading
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
The newer paper generalizes the earlier CPC embedding concept into a formal contrastive dictionary look-up framework with normalized query/key vectors, building directly on cited prior contrastive works.</br>— the tool’s reading
“patchified inputs [46, 35], carefully tailored receptive fields [2], or combining two networks [56].”
“removed [14, 46, 35], and R170 is made wider [35]; Rv50 is a reversible”
“Several recent studies [61, 46, 36, 66, 35, 56, 2] present promising results on unsupervised visual representation learning using approaches related to the contrastive loss [29].”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
Both use encoder-derived dense embeddings, but the newer paper generalizes this into a formal contrastive query/key framework with InfoNCE loss and a shared representation space.confidence— the tool’s reading
“1a) [13, 8, 17, 1, 9, 2], the negative keys are from the same batch and updated end-to-end by back-propagation.”
“Recent studies on unsupervised representation learning from images [16, 13, 8, 17, 1, 9, 15, 6, 12, 2] are converging on a central concept known as contrastive learning [5].”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
The newer paper builds on the same self-supervised embedding concept, adding a projection MLP and dual-view similarity comparison beyond simple encoder pooling.confidence— the tool’s reading
“This methodology has been recently popularized for un-/self-supervised representation learning [36, 30, 20, 37, 21, 2, 35, 17, 29, 8, 9].”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
Both use 'embedding' for encoder-derived dense vectors in self-supervised learning, with newer paper generalizing to projector outputs compared across views via a new loss.on— the tool’s reading
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
“We call the output of the encoder the 'representations' and the output of the projector the 'embeddings'. The representations are used for downstream tasks and the embeddings are fed to the loss function of Barlow Twins.”
SimCLR restricts 'embedding' to a specific L2-normalized projected vector used solely for contrastive loss computation, a more constrained role than the general encoder representation in CPC.— the tool’s reading
“• CPC v1 and v2 (Oord et al., 2018; Hénaff et al., 2019) define the context prediction task using a deterministic strategy to split examples into patches, and a context aggregation network (a PixelCNN) to aggregate these patches.”
“Although previous work has reported that data augmentation is useful for self-supervised learning (Doersch et al., 2015; Bachman et al., 2019; Hénaff et al., 2019; Asano et al., 2019), we show that data augmentation that does not yield accuracy benefits for supervised learning can still help…”
“When fine-tuned with only 1% of the ImageNet labels, SimCLR achieves 85.8% top-5 accuracy, a relative improvement of 10% (Hénaff et al., 2019).”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper builds on the same CPC-style dense embedding notion but elaborates it into explicit parameterized projection functions for similarity-based mutual information estimation.dersom— the tool’s reading
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
The newer paper restricts embedding to KG entity/relation representations for link prediction via scoring functions, dropping the recommendation-specific attention propagation mechanism.— the tool’s reading
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
“Recent advances in knowledge-graph-based research focus on knowledge representation learning (KRL) or knowledge graph embedding (KGE) by mapping entities and relations into low-dimensional vectors while capturing their semantic meanings <cit.>.”
Both learn dense vector representations from images via deep networks for downstream use, but the newer paper generalizes to self-supervised augmentation-invariant clustering rather than adversarial encoding.— the tool’s reading
“The encoder ℰ models the inverse conditional distribution P(𝐳|𝐱), predicting latents 𝐳 given data 𝐱 sampled from the data distribution P_𝐱.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
Both use encoder-derived dense embeddings for representation learning, but the newer paper generalizes learning via contrastive dictionary look-up instead of adversarial training.'— the tool’s reading
“Adversarial methods for representation learning are explored in [15, 16].”
“The encoder ℰ models the inverse conditional distribution P(𝐳|𝐱), predicting latents 𝐳 given data 𝐱 sampled from the data distribution P_𝐱.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
The newer paper restricts embeddings to L2-normalized vectors used specifically in contrastive loss, a specialized case of the earlier general adversarially-learned latent representation.— the tool’s reading
“The encoder ℰ models the inverse conditional distribution P(𝐳|𝐱), predicting latents 𝐳 given data 𝐱 sampled from the data distribution P_𝐱.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper specializes learned similarity-space embeddings to L2-normalized ConvNet outputs compared against prototypes, a specific self-supervised clustering instantiation of the earlier general embedding framework.— the tool’s reading
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
The newer paper simplifies the earlier's parameterized similarity-projection functions into a generic pooled encoder output used directly as input for downstream tasks, restricting its use.confidence— the tool’s reading
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
The newer paper adopts embedding as a generic dense vector representation from an encoder, treating the earlier NCE-specific learned projection function as one instance within broader contrastive/generative representation learning.— the tool’s reading
“Among discriminative methods, contrastive methods [9, 10, 32, 33, 34, 11, 35, 36] currently achieve state-of-the-art performance in self-supervised learning [37, 8, 38, 12].”
“In addition, their performance critically depends on the choice of image augmentations [34, 11, 8, 12].”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
The newer paper simplifies the earlier's explicit parameterized similarity-projection functions into a generic dense vector representation concept for contrastive learning, restricting scope.— the tool’s reading
“For example: luminance and chrominance decomposition [53], randomly augmenting an image twice [58, 7, 4, 23, 60, 50, 65, 67], using different time-steps of videos [42, 66, 46, 22, 21], patches of the same image [27, 42, 26], multiple sensory data [39, 9, 44], or text and its context [37, 59, 35, 32].”
“In practice, v1 and v2 are two views of the data x, such as different augmentations of the same image [58, 4, 23, 8, 7], different image channels [53], or video and text pairs [52, 36, 33].”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“(a) Schematic of multiview contrastive representation learning, where an image is split into two views, and passed through two encoders to learn an embedding where the views are close relative to views from other images.”
Both use embedding as learned vector representation for similarity-based contrastive/MI objectives, with newer paper simplifying terminology into generic representation vector v_I without altering core concept.— the tool’s reading
“4%, which is close to the accuracy obtained by AMDIM [4] with a model that has 6× more parameters.”
“PIRL-c2x achieves a top-1 accuracy of 67.4%, which is close to the accuracy obtained by AMDIM [4] with a model that has 6× more parameters.”
“PIRL is most similar to methods that learn representations that are invariant under standard data augmentation [4, 14, 29, 31, 81, 82].”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
Both describe encoder-projected feature vectors compared via dot-product similarity for contrastive/NCE losses, with the newer paper using 'representation' instead of 'embedding' terminology.— the tool’s reading
“1a) [13, 8, 17, 1, 9, 2], the negative keys are from the same batch and updated end-to-end by back-propagation.”
“Recent studies on unsupervised representation learning from images [16, 13, 8, 17, 1, 9, 15, 6, 12, 2] are converging on a central concept known as contrastive learning [5].”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
The newer paper adopts embedding as encoder output for self-supervised image views, a specific instantiation of the earlier general learned similarity-projection framework.— the tool’s reading
“Simple and effective instantiations of contrastive learning have been developed using Siamese networks [37, 2, 17, 8, 9].”
“This methodology has been recently popularized for un-/self-supervised representation learning [36, 30, 20, 37, 21, 2, 35, 17, 29, 8, 9].”
“, [2, 17, 8, 15, 7]).”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
The newer paper restricts the earlier's general learned similarity-space projections to L2-normalized vectors with cosine similarity plus temperature, a specific special case for contrastive loss.— the tool’s reading
“The best self-supervised model that reports linear evaluation result on CIFAR-10 is AMDIM (Bachman et al., 2019), which achieves 91.2% with a model 25 × larger than ours.”
“Although previous work has reported that data augmentation is useful for self-supervised learning (Doersch et al., 2015; Bachman et al., 2019; Hénaff et al., 2019; Asano et al., 2019), we show that data augmentation that does not yield accuracy benefits for supervised learning can still help…”
“Discriminative approaches based on contrastive learning in the latent space have recently shown great promise, achieving state-of-the-art results (Hadsell et al., 2006; Dosovitskiy et al., 2014; Oord et al., 2018; Bachman et al., 2019).”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper generalizes the earlier learned similarity-space projections into a broader encoder-based embedding framework used across contrastive and prototype-based objectives.rate— the tool’s reading
“Image transformation aims to generate multiple embeddings that represent the same image, by data augmentation [2, 8, 9], patch perturbation [4], or using momentum features [3].”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“unsupervised visual representation learning aims to learn an embedding function f_θ (realized via a DNN) that maps X to V={v_1, v_2,...,v_n} with v_i=f_θ(x_i), such that v_i best describes x_i.”
The newer paper simplifies the earlier multi-view, dot-product NCE embedding functions into a single encoder producing a compact latent vector for similarity via distance metrics.— the tool’s reading
“A preliminary exploration of this is given in Section 4.1.2, where we achieve 60.1% top-1 accuracy on ImageNet with CMC using Resnet-101, compared to 61.0% with CPC++ using Resnet-170 and 60.2% with Deep Infomax++ using a heavily-customized Resnet.”
“To our best knowledge, CMC, CPC++, and Deep Infomax++ form the first batch of unsupervised/self-supervised learning algorithms which surpass the supervised AlexNet for ImageNet classification.”
“Concurrently with our work new papers have appeared on arXiv in the last few weeks: Deep Infomax++[3], CPC++[22] and [64].”
“Parameters ndf and nrkhs determine encoder feature dimension and output dimension for the embedding functions ϕ_n(f_n). The embeddings ϕ_7(f_7) and ϕ_5(f_5) are computed by applying a small MLP via convolution.”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
SwAV builds on the encoder-derived contrastive embedding idea but replaces pairwise instance comparisons with cluster/prototype assignments, avoiding memory banks and momentum encoders.》— the tool’s reading
“Finally, note that SwAV could be combined with a momentum mechanism and a large queue [20]; we leave these explorations to future work.”
“In addition, SwAV works with small and large batch sizes and does not need a large memory bank [49] or a momentum encoder [20].”
“Unsupervised visual representation learning, or self-supervised learning, aims at obtaining features without using manual annotations and is rapidly closing the performance gap with supervised pretraining in computer vision [9, 20, 37].”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
The newer paper restricts the embedding concept to a plain encoder output used for downstream tasks, omitting the contrastive dictionary-lookup and normalization framing.atur— the tool’s reading
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“Having trained an encoder network f_θ, a context network g_ϕ, and a set of linear predictors {W_k} using the CPC objective, we use the encoder to form a representation z=f_θ(x) of new observations x, and discard the rest.”
BYOL generalizes the encoder-derived embedding concept from contrastive negative-pair comparison to a broader representation learned without negatives, citing MoCo as baseline.'— the tool’s reading
“Similarly, we evaluate on object detection by reproducing the setup in [9] using a Faster R-CNN architecture [82], as detailed in Appendix E.”
“Method AP50 mIoU Supervised-IN [9] 74.4 74.4 MoCo [9] 74.9 72.5 SimCLR (repro) 75.2 75.2 BYOL (ours) 77.5 76.3 (a) Transfer results in semantic segmentation and object detection.”
“Contrastive methods often require comparing each example with many other examples to work well [9, 8] prompting the question of whether using negative pairs is necessary.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
The newer paper generalizes contrastive encoder embeddings into a broader dense vector representation used for curation, retrieval, and downstream tasks, building on the earlier method.— the tool’s reading
“The DINO loss term corresponds to: We learn the parameters of the student and build the teacher head with an exponential moving average of past iterates (He et al., 2020).”
“…augments the initial dataset through a self-supervised retrieval system. were made based either on instance-level objectives (Hénaff et al., 2019; He et al., 2020; Chen & He, 2021; Chen et al., 2020; Grill et al., 2020; Caron et al., 2021) or clustering (Caron et al., 2018; Asano et al., 2020;…”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“In order to do this, we first compute an image embedding using a self-supervised ViT-H/16 network pretrained on ImageNet-22k, and use cosine-similarity as a distance measure between images.”
The newer paper cites MoCo as one instance within a broader survey framework, applying the same embedding concept but to a narrower specific setup of two-view image augmentations.— the tool’s reading
“3 MoCo [23] ResNet-50 24 Linear 200 60.”
“Below, we consider several recent works in this framework: InstDis [58] and MoCo [23].”
“For example: luminance and chrominance decomposition [53], randomly augmenting an image twice [58, 7, 4, 23, 60, 50, 65, 67], using different time-steps of videos [42, 66, 46, 22, 21], patches of the same image [27, 42, 26], multiple sensory data [39, 9, 44], or text and its context [37, 59, 35, 32].”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“(a) Schematic of multiview contrastive representation learning, where an image is split into two views, and passed through two encoders to learn an embedding where the views are close relative to views from other images.”
Both use encoder-derived normalized vectors for contrastive comparison, with newer paper using 'embedding' interchangeably with 'representation/feature vector' rather than altering the core mechanism.— the tool’s reading
“Similar to concurrent work [26], we find that a self-supervised learner can outperform supervised pre-training for object detection.”
“We note that PIRL also outperforms concurrent work [26] in this setting.”
“Finally, PIRL is also related to approaches that use a contrastive loss [24] in predictive learning [25, 26, 28, 59, 70, 73].”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
The newer paper drops the dictionary/queue and negative-pair mechanics, using embeddings purely as similarity-compared output vectors between two augmented views without momentum encoder or memory bank.— the tool’s reading
“In a Siamese network, MoCo [17] maintains a queue of negative samples and turns one branch into a momentum encoder to improve consistency of the queue.”
“This methodology has been recently popularized for un-/self-supervised representation learning [36, 30, 20, 37, 21, 2, 35, 17, 29, 8, 9].”
“Simple and effective instantiations of contrastive learning have been developed using Siamese networks [37, 2, 17, 8, 9].”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
Both use encoder-derived embeddings for self-supervised representation learning, but the newer paper generalizes the mechanism via a projector and cross-correlation loss instead of contrastive dot-product similarity.— the tool’s reading
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“We call the output of the encoder the 'representations' and the output of the projector the 'embeddings'. The representations are used for downstream tasks and the embeddings are fed to the loss function of Barlow Twins.”
SimCLR builds on encoder-based contrastive embeddings, refining normalization and adding a learned projection head plus temperature scaling, without a memory bank.rated— the tool’s reading
“Other approaches include shuffling data examples (He et al., 2019), or replacing BN with layer norm (Hénaff et al., 2019).”
“A similar consistency idea has been explored in other contexts such as semi-supervised learning (Xie et al., 2019; Berthelot et al., 2019).”
“Not only does SimCLR outperform previous work (Figure 1), but it is also simpler, requiring neither specialized architectures (Bachman et al., 2019; Hénaff et al., 2019) nor a memory bank (Wu et al., 2018; Tian et al., 2019; He et al., 2019; Misra & van der Maaten, 2019).”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper builds on the encoder-derived embedding space by adding prototype-based objectives (ProtoNCE) to enrich semantic structure beyond MoCo's InfoNCE approach.'— the tool’s reading
“Unsupervised visual representation learning aims to learn image representations from pixels themselves without relying on semantic annotations, and recent advances are largely driven by instance discrimination tasks [1, 2, 3, 4, 5, 6, 7].”
“Instance-wise contrastive learning achieves this objective by optimizing a contrastive loss function, such as InfoNCE [6, 3], defined as:”
“Therefore, to enable a fair and direct comparison with previous methods, we follow the same setting for unsupervised training as MoCo [3].”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“unsupervised visual representation learning aims to learn an embedding function f_θ (realized via a DNN) that maps X to V={v_1, v_2,...,v_n} with v_i=f_θ(x_i), such that v_i best describes x_i.”
Both use encoder-derived embeddings for contrastive similarity learning, with the newer paper generalizing the approach via stronger augmentation and broader comparison to state-of-the-art methods.'— the tool’s reading
“On top of it, strengthening data augmentation with RandAugment [13] yields better or comparable results to other state-of-the-art methods [40,79,87,26,48,18,28,4].”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
“Given a set of sensory views, a deep representation is learnt by bringing views of the same scene together in embedding space, while pushing views of different scenes apart.”
Both treat embeddings as learned normalized feature vectors for contrastive-style comparison, but the newer paper adds prototype-based cluster assignment, generalizing the earlier pairwise projection approach.— the tool’s reading
“Unsupervised visual representation learning, or self-supervised learning, aims at obtaining features without using manual annotations and is rapidly closing the performance gap with supervised pretraining in computer vision [9, 20, 37].”
“Of particular interest, Misra and van der Maaten [43] propose to encode the jigsaw puzzle task [45] as an invariant for contrastive learning.”
“Both elements are essential to the quality of the resulting networks [9, 37] and our work improves upon both the objective function and the transformations.”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
Both papers describe encoder-produced embedding vectors compared contrastively, with the newer paper using 'representation'/query-key terminology for essentially the same concept.— the tool’s reading
“Recent studies on unsupervised representation learning from images [16, 13, 8, 17, 1, 9, 15, 6, 12, 2] are converging on a central concept known as contrastive learning [5].”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
Newer paper restricts embedding usage to self-supervised, label-free contrastive setting comparing augmented views, a special case of the earlier supervised contrastive framework.— the tool’s reading
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
Barlow Twins adopts the same joint-embedding notion from contrastive SSL methods but applies a new cross-correlation loss, generalizing the embedding comparison mechanism rather than contesting or narrowing it.— the tool’s reading
“We follow the exact settings from PIRL (Misra & van der Maaten, 2019) for evaluating linear classifiers on the Places-205, VOC07 and iNaturalist2018 datasets.”
“Like other methods for SSL (Caron et al., 2020; Chen et al., 2020a; Grill et al., 2020; He et al., 2019; Misra & van der Maaten, 2019), B ARLOW T WINS operates on a joint embedding of distorted images (Fig.”
“Image classification with fixed features We follow the setup from (Misra & van der Maaten, 2019) and train a linear classifier on fixed image representations, i.e ., the et al., 2014) for scene classification, VOC07 (Everingham et al., 2010) for multi-label image classification, and iNat-uralist2018 (Van…”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
“We call the output of the encoder the 'representations' and the output of the projector the 'embeddings'. The representations are used for downstream tasks and the embeddings are fed to the loss function of Barlow Twins.”
SimCLR restricts the general embedding notion to L2-normalized projected vectors with a temperature-scaled contrastive loss, a more specific formulation than the earlier generic feature vector.— the tool’s reading
“Not only does SimCLR outperform previous work (Figure 1), but it is also simpler, requiring neither specialized architectures (Bachman et al., 2019; Hénaff et al., 2019) nor a memory bank (Wu et al., 2018; Tian et al., 2019; He et al., 2019; Misra & van der Maaten, 2019).”
“Wu et al. (2018) proposes to use a memory bank to store the instance class representation vector, an approach adopted and extended in several recent papers (Zhuang et al., 2019; Tian et al., 2019; He et al., 2019; Misra & van der Maaten, 2019).”
“Specifically, we compute the representation of 𝐈, f(𝐯_𝐈), by extracting features, average pooling, and a linear projection to obtain a 128-dimensional representation.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
The newer paper builds on translation-based KG/user-item embeddings by adding recursive neighbor propagation and attention for high-order connectivity, generalizing the earlier approach.— the tool’s reading
“We organize the side information in the form of knowledge graph G2, which is a directed graph composed of subject-property-object triple facts [5].”
“For example, KTUP [5] and CFKG [1] jointly train”
“For example, KTUP [5] and CFKG [1] jointly train the two tasks of recommendation and KG completion with shared item embeddings.”
“Its basic idea is to learn embeddings for entities and relations, satisfying 𝐞_h+𝐫≈𝐞_t if there is a triplet (e_h, e_t, r) in KG.”
“It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors.”
The newer paper builds on the same embedding paradigm but generalizes it by adding explicit high-order collaborative signal via message-passing propagation, beyond translation-based scoring.— the tool’s reading
“Following mainstream recommender models [1, 14, 26], we describe a user u (an item i) with an embedding vector eu ∈ Rd (ei ∈ Rd ), where d denotes the embedding size.”
“To implement the assumption, a common paradigm is to parameterize users and items for reconstructing historical interactions, and predict user preference based on the parameters [1, 14].”
“Its basic idea is to learn embeddings for entities and relations, satisfying 𝐞_h+𝐫≈𝐞_t if there is a triplet (e_h, e_t, r) in KG.”
“Generally speaking, there are two key components in learnable CF models — 1) embedding, which transforms users and items to vectorized representations, and 2) interaction modeling, which reconstructs historical interactions based on the embeddings.”
The newer paper cites the anomaly-detection embedding work merely as an application example while repurposing 'embedding' for knowledge distillation between teacher-student networks, a different task domain.'— the tool’s reading
“, 2020c), anomaly detection (Bergmann et al., 2020), facial landmark detection (Dong and Yang, 2019), video classification (Bhardwaj et al.”
“, 2019), image or video segmentation (He et al., 2019; Liu et al., 2019h; Mullapudi et al., 2019; Siam et al., 2019; Dou et al., 2020; Hou et al., 2020; Bergmann et al., 2020), person re-identification (Wu et al.”
“…et al. 2020b), image/video segmentation (He et al. 2019; Mullapudi et al. 2019; Dou et al. 2020; Liu et al. 2019h; Siam et al. 2019; Hou et al. 2020; Bergmann et al. 2020), action recognition (Luo et al. 2018; Hao and Zhang 2019; Thoker and Gall 2019; Garcia et al. 2018; Wang et al. 2019e; Wu et…”
“The performance of many supervised computer vision algorithms <cit.> is improved by transfer learning, i.e. by using discriminative embeddings from pretrained networks.”
“Based on idea of manifold learning, the student network is learned by feature embedding, which preserves the feature similarities of samples in the intermediate layers of the teacher networks <cit.>.”
The newer paper generalizes static token embeddings into contextual, context-dependent embeddings from models like BERT, building on the earlier dense vector representation concept.— the tool’s reading
“…inference, sentiment analysis, paraphrase detection, question answering, named entity recognition (Devlin et al., 2019), text summarization (Liu, 2019), contextual emotion detection (Huang et al., 2019), citation recommendation (Jeong et al., 2019), and document retrieval (Yang et al., 2019).”
“and e(x) denotes the embedding of x.”
“In contrast to prior word embeddings <cit.>, contextual embeddings, such as BERT <cit.> and ELMo <cit.>, can generate different vector representations for the same word in different sentences depending on the surrounding words, which form the context of the target word.”
The newer paper reuses the L2-normalized embedding concept from SimCLR but extends it by clustering against trainable prototypes and adding multi-crop augmentation for improved performance.— the tool’s reading
“We also show that our multi-crop strategy is general, and improves the performance of different self-supervised methods, namely SimCLR [9], DeepCluster [6], and SeLa [2], between 2% and 4% top-1 accuracy on ImageNet.”
“Unsupervised visual representation learning, or self-supervised learning, aims at obtaining features without using manual annotations and is rapidly closing the performance gap with supervised pretraining in computer vision [9, 20, 37].”
“Both elements are essential to the quality of the resulting networks [9, 37] and our work improves upon both the objective function and the transformations.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
The newer paper adopts a broader, general notion of embeddings as encoder outputs for representation learning, treating the earlier normalized contrastive-specific vectors as one special instantiation among generative/discriminative methods.— the tool’s reading
“Most unsupervised methods for representation learning can be categorized as either generative or discriminative [23, 8].”
“In addition, their performance critically depends on the choice of image augmentations [34, 11, 8, 12].”
“Yet, even with suitable architectures [48], these methods are being outperformed by contrastive methods [37, 8, 12].”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
The newer paper builds on the earlier normalized-embedding contrastive formulation, generalizing it into a dictionary look-up framework with encoder-derived queries/keys.ed.— the tool’s reading
“As an example, “MoCo v2” [8], an extension of a prelim-inary version of this manuscript, achieves 71.1% accuracy with R50 (up from 60.6%), given small changes on the data augmentation and output projection head [7].”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“In general, the query representation is q=f_q(x^q) where f_q is an encoder network and x^q is a query sample (likewise, k=f_k(x^k)).”
The newer paper generalizes 'embedding' from a normalized contrastive-loss vector to a broader dense representation used for retrieval, clustering, and downstream tasks.— the tool’s reading
“For our semantic segmentation evaluation, we consider two different setups.”
“5, the segmentation training in this experiment took 28 hours on 16 V100 GPUs.”
“An alternative to text-guided pretraining is self-supervised learning (Caron et al., 2018; Chen et al., 2020; He et al., 2022) where features are learned from images alone.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“In order to do this, we first compute an image embedding using a self-supervised ViT-H/16 network pretrained on ImageNet-22k, and use cosine-similarity as a distance measure between images.”
The newer paper drops the L2-normalization/temperature specifics, treating embedding generically as any learned representation space where positive views cluster, a broader but less precise usage than the earlier normalized contrastive formulation.— the tool’s reading
“For example: luminance and chrominance decomposition [53], randomly augmenting an image twice [58, 7, 4, 23, 60, 50, 65, 67], using different time-steps of videos [42, 66, 46, 22, 21], patches of the same image [27, 42, 26], multiple sensory data [39, 9, 44], or text and its context [37, 59, 35, 32].”
“In combination of the JigSaw strategy proposed in PIRL [38], our InfoMin Aug achieves 73.0% top-1 accuracy on ImageNet linear readout benchmark with ResNet-50, outperforming SimCLR [7] by nearly 4%, as shown in Table 1.”
“In practice, v1 and v2 are two views of the data x, such as different augmentations of the same image [58, 4, 23, 8, 7], different image channels [53], or video and text pairs [52, 36, 33].”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“(a) Schematic of multiview contrastive representation learning, where an image is split into two views, and passed through two encoders to learn an embedding where the views are close relative to views from other images.”
Both use L2/dot-product-compared latent vectors for InfoNCE-style contrastive loss, but the newer paper relabels normalized projected embeddings as 'representations' (query/key) rather than altering the underlying concept.— the tool’s reading
“…., Momentum Contrast (MoCo) [6] shows that unsupervised pre-training can surpass its ImageNet-supervised counterpart in multiple detection and segmentation tasks, and SimCLR [2] further reduces the gap in linear classifier performance between unsupervised and supervised pre-training representations.”
“1a) [13, 8, 17, 1, 9, 2], the negative keys are from the same batch and updated end-to-end by back-propagation.”
“The end-to-end case re-flects the SimCLR cost in GPUs (instead of TPUs in [2]).”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
SimSiam removes negative samples and normalization-driven contrastive loss, restricting the embedding concept to a simpler stop-gradient Siamese setup without contrastive comparisons.— the tool’s reading
“ectures. Our method plays as a hub to connect these methods. Figure3abstracts these methods. The “encoder” subsumes all layers that can be shared between both branches (e.g., backbone, projection MLP [8], prototypes [7]). The components in red are those missing in SimSiam. We discuss the relations next. Relation to SimCLR [8]. SimCLR relies on negative sampl…”
“tch size is 512 by default, which is friendly to typical 8-GPU implementations. Other batch sizes also work well (Sec.4.3). We use batch normalization (BN) [22] synchronized across devices, following [8,15,7]. • Projection MLP. The projection MLP (in f) has BN applied to each fully-connected (fc) layer, including its output fc. Its output fc has no ReLU. The…”
“curacy. The results are similarly good when the batch size is from 256 to 2048, and the differences are at the level of random variations. This behavior of SimSiam is noticeably different from SimCLR [8] and SwAV [7]. All three methods are Siamese networks with direct weight-sharing, but SimCLR and SwAV both require a large batch (e.g., 4096) to work well. W…”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
The newer paper replaces normalized contrastive similarity with a cross-correlation decorrelation loss, explicitly contrasting its embedding scaling behavior with SimCLR's saturation.— the tool’s reading
“This intriguing result is in stark contrast with other popular methods for SSL, such as S IM CLR (Chen et al., 2020a) and BYOL (Grill et al., 2020), for which increasing the dimensionality of the embeddings rapidly saturates performance.”
“S IM CLR (Chen et al., 2020a)).”
“For example, MoCo (Chen et al., 2020b; He et al., 2019) builds a dynamic dictionary of negative samples with a queue and a moving-averaged encoder.”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“We call the output of the encoder the 'representations' and the output of the projector the 'embeddings'. The representations are used for downstream tasks and the embeddings are fed to the loss function of Barlow Twins.”
The newer paper builds on SimCLR's normalized contrastive embedding, adding prototype-based clustering objectives while retaining InfoNCE and projection head design as generalized components.— the tool’s reading
“Note that Equation 10 has a similar form as the InfoNCE loss in Equation 1.”
“The end-to-end approach [2, 7, 9] generates instance features using all samples within the current mini-batch, and apply the same encoder to both the original samples and their augmented version.”
“We perform additional experiments using a non-linear projection head (a 2-layer MLP), which has been shown to improve representation learning [9, 33].”
“Representation learning with contrastive cross entropy loss benefits from normalized embeddings and an appropriately adjusted temperature parameter.”
“unsupervised visual representation learning aims to learn an embedding function f_θ (realized via a DNN) that maps X to V={v_1, v_2,...,v_n} with v_i=f_θ(x_i), such that v_i best describes x_i.”
The newer paper adopts the same embedding-as-encoder-output notion but restricts it specifically to self-supervised contrastive/predictive comparison between augmented views, without labels or explicit generative distribution modeling.-— the tool’s reading
“We use batch normalization (BN) [22] synchronized across devices, following [8, 15, 7].”
“Our method does not require a large-batch optimizer such as LARS [38] (unlike [8, 15, 7]).”
“BYOL [15] directly predicts the output of one view from another view.”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
The newer paper specializes the general encoder-output embedding concept to a specific self-supervised twin-view projector output compared via cross-correlation, a special case of the earlier broad representation notion.— the tool’s reading
“, 2020a) fig 8; Data for BYOL is from (Grill et al., 2020) Table 14b.”
“Data for BYOL and SIMCLR (repro) is from (Grill et al., 2020) fig 3b.”
“L ATENT (aka BYOL) (Grill et al., 2020) and S IM S IAM (Chen & He, 2020) are two recent methods which use a simple co-sine similarity between twin embeddings as an objective function, without any contrastive term: Surprisingly, these methods successfully avoid trivial solutions by introducing some…”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
“We call the output of the encoder the 'representations' and the output of the projector the 'embeddings'. The representations are used for downstream tasks and the embeddings are fed to the loss function of Barlow Twins.”
The newer paper generalizes the same self-supervised embedding vector concept to broader uses like retrieval, clustering, deduplication, and downstream frozen features.rationale— the tool’s reading
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
“In order to do this, we first compute an image embedding using a self-supervised ViT-H/16 network pretrained on ImageNet-22k, and use cosine-similarity as a distance measure between images.”
Both use encoder-derived normalized embeddings for contrastive-style comparison, but the newer paper extends this to cluster-prototype assignments instead of instance-level negatives.'— the tool’s reading
“4× slower than MoCov2 due to the additional back-propagation [10].”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
Both describe encoder-produced vector representations for images, with newer paper using 'embedding' interchangeably with 'representation' from the earlier contrastive framing.'— the tool’s reading
“Images are encoded into a representation space, in which pairwise affinities are computed.”
“Generative approaches to representation learning build a distribution over data and latent embedding and use the learned embeddings as image representations.”
Both describe encoder-produced contrastive representation vectors mapping similar views close together, just using 'embedding' instead of 'representation'.— the tool’s reading
“In practice, v1 and v2 are two views of the data x, such as different augmentations of the same image [58, 4, 23, 8, 7], different image channels [53], or video and text pairs [52, 36, 33].”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
“(a) Schematic of multiview contrastive representation learning, where an image is split into two views, and passed through two encoders to learn an embedding where the views are close relative to views from other images.”
Both use encoder-output vectors for contrastive-style representation learning, but the newer paper drops explicit query/key terminology, calling it simply 'embedding' z.— the tool’s reading
“Simple and effective instantiations of contrastive learning have been developed using Siamese networks [37, 2, 17, 8, 9].”
“This methodology has been recently popularized for un-/self-supervised representation learning [36, 30, 20, 37, 21, 2, 35, 17, 29, 8, 9].”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
“Denoting the two output vectors as p_1≜h(f(x_1)) and z_2≜f(x_2), we minimize their negative cosine similarity:”
Both use encoder-derived vectors for contrastive-style self-supervised representation learning, but the newer paper relabels 'representation' vectors as 'embeddings' with a projector and decorrelation loss instead of InfoNCE.— the tool’s reading
“This intriguing result is in stark contrast with other popular methods for SSL, such as S IM CLR (Chen et al., 2020a) and BYOL (Grill et al., 2020), for which increasing the dimensionality of the embeddings rapidly saturates performance.”
“S IM CLR (Chen et al., 2020a)).”
“For example, MoCo (Chen et al., 2020b; He et al., 2019) builds a dynamic dictionary of negative samples with a queue and a moving-averaged encoder.”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
“We call the output of the encoder the 'representations' and the output of the projector the 'embeddings'. The representations are used for downstream tasks and the embeddings are fed to the loss function of Barlow Twins.”
Both describe encoder-produced vector representations from images used in contrastive learning; newer paper relabels 'representation' as 'embedding space' without fundamentally altering the concept.— the tool’s reading
“6 Implementation details It has been shown that the performance of unsupervised learned representations can be improved by using a larger network, adopting stronger data augmentation, training for more epochs, or using a larger batchsize [33, 9, 34, 4].”
“We perform additional experiments using a non-linear projection head (a 2-layer MLP), which has been shown to improve representation learning [9, 33].”
“Images are encoded into a representation space, in which pairwise affinities are computed.”
“unsupervised visual representation learning aims to learn an embedding function f_θ (realized via a DNN) that maps X to V={v_1, v_2,...,v_n} with v_i=f_θ(x_i), such that v_i best describes x_i.”
The newer paper adopts the same embedding-space/prototype framework but specializes it to L2-normalized, augmentation-invariant embeddings from a self-supervised ConvNet, a narrower instantiation of the earlier general embedding concept.— the tool’s reading
“We mostly follow hyperparameters from PCL [36]: we train during 20 epochs with a batch size of 256 , we use distinct learning rates for the convnet weights and the final linear layer, and we decay the learning rates by a factor 0 .”
“Similarly to previous works [10, 11, 36], we use a projection head on top of the convnet features that consists in a 2 -layer multi-layer perceptron (MLP) that projects the convnet output to a 128 -”
“unsupervised visual representation learning aims to learn an embedding function f_θ (realized via a DNN) that maps X to V={v_1, v_2,...,v_n} with v_i=f_θ(x_i), such that v_i best describes x_i.”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
The newer paper generalizes embeddings from cluster-assignment features to broader dense representations for retrieval, curation, and downstream tasks, building on SwAV's centering technique.'— the tool’s reading
“• Sinkhorn-Knopp centering (Caron et al., 2020).”
“(2022) recommend to replace the teacher softmax-centering step of DINO and iBot by the Sinkhorn-Knopp (SK) batch normalization of SwAV (Caron et al., 2020).”
“…dataset through a self-supervised retrieval system. were made based either on instance-level objectives (Hénaff et al., 2019; He et al., 2020; Chen & He, 2021; Chen et al., 2020; Grill et al., 2020; Caron et al., 2021) or clustering (Caron et al., 2018; Asano et al., 2020; Caron et al., 2020).”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
“In order to do this, we first compute an image embedding using a self-supervised ViT-H/16 network pretrained on ImageNet-22k, and use cosine-similarity as a distance measure between images.”
The newer paper generalizes the earlier ConvNet output-vector notion into a broader learned embedding space shaped by contrastive and prototype objectives, addressing DeepCluster's weaknesses.— the tool’s reading
“In DeepCluster, the cluster assignments are considered as pseudo-labels and a classification objective is optimized, which results in two weaknesses: (1) the high-dimensional features from the penultimate layer of a ConvNet are not optimal for clustering and need to be PCA-reduced; (2) an additional…”
“[40, 39] ResNet-50v2 - 47.0 83.4 S 4 L Exemplar [39] ResNet-50v2 - 47.0 83.7 S 4 L Rotation [39] ResNet-50v2 - 53.4 83.8 Self-supervised learning methods: Instance Discrimination [1] ResNet-50 200 39.2 77.4 Jigsaw [24, 4] ResNet-50 90 45.3 79.3 SimCLR [8] ResNet-50-MLP 200 56.5 82.7 MoCo [3]…”
“The feature is then projected to the unit sphere, i.e., 𝐳_nt = f_θ(𝐱_nt) / f_θ(𝐱_nt)_2.”
“unsupervised visual representation learning aims to learn an embedding function f_θ (realized via a DNN) that maps X to V={v_1, v_2,...,v_n} with v_i=f_θ(x_i), such that v_i best describes x_i.”