For this , we present a co-occurrence embedding regularized metric learning model ( CRML ) for collaborative recommendations . We consider the optimization problem as a multi-task learning problem which includes optimizing a primary task of metric learning and two auxiliary tasks of representation learning . In particular , we develop an effective approach for learning the embedding representations of both users and items , and then exploit the strategy of soft parameter sharing to optimize the model parameters . Current deep clustering methods usually boost the clustering results by means of the powerful representation ability of deep learning , e.g. , autoencoder , suggesting that learning an effective representation for clustering is a crucial requirement . The strength of deep clustering methods is to extract the useful representations from the data itself , rather than the structure of data , which receives scarce attention in representation learning . Motivated by the great success of Graph Convolutional Network ( GCN ) in encoding the graph structure , we propose a Structural Deep Clustering Network ( SDCN ) to integrate the structural information into deep clustering . A few studies explored utilizing temporal information available in a dynamic setting in order to address the node representation learning . However , the representation learning problem for the entire graph in a dynamic context is yet to be addressed . In this paper , we propose an unsupervised encoder-decoder framework that projects a dynamic graph at each time step into a d-dimensional space , taking into account both the graph 's topology and dynamics . We focus on metric learning due to the importance of computing distances between examples in learning algorithms and present Jiffy , a simple and scalable distance metric learning method for multivariate time series . Our approach is to reframe the task as a representation learning problem – rather than design an elaborate distance function , we use a CNN to learn an embedding such that the Euclidean distance is effective . Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods . section : Related Work subsection : Representation Learning from unlabeled data Unsupervised representation learning is a fairly well studied problem in general computer vision research , as well as in the context of images . As a result , the learned model is biased towards the synthetic data , which leads to an unsatisfactory segmentation performance on real urban scene images . From the perspective of representation learning , this is largely because that the convolutional filters in the semantic segmentation models overfit to the synthetic image style . Consequently , when taking a real image as input , the feature representation generated using the learned model might not be sufficiently informative for semantic segmentation , since the convolutional filters tend to extract information that look discriminative in synthetic style only . Similarly , it is useful for multiple ( related ) tasks to be learned jointly so that the knowledge learned in one task can benefit other tasks . Recently , there is a growing interest in applying MTL to representation learning using deep neural networks ( DNNs ) collobert2011natural , liu2015mtl , luong2015multi , mt - mrc2018 for two reasons . First , supervised learning of DNNs requires large amounts of task - specific labeled data , which is not always available . Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction , node classification , and graph visualization . Previous methods on graph representation learning mainly focus on static graphs , however , many real-world graphs are dynamic and evolve over time . In this paper , we present Dynamic Self-Attention Network ( DySAT ) , a novel neural architecture that operates on dynamic graphs and learns node representations that capture both structural properties and temporal evolutionary patterns . Graph Convolutional Networks ( GCNs ) have emerged as the state-of-the-art deep learning model for representation learning on graphs . It is challenging to accelerate training of GCNs , due to ( 1 ) substantial and irregular data communication to propagate information within the graph , and ( 2 ) intensive computation to propagate information along the neural network layers . Abstract The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces . Mostly current works have demonstrated the benefits of knowledge graph embedding in single knowledge graph completion , such as relation extraction . We introduce a novel approach to graph-level representation learning , which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity . Our approach , UGRAPHEMB , is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner . However , these methods fail to capture the co-occurrence relationship between more complex sub-structures in the graph since they were designed primarily for node representation learning . In this work , we study the problem of learning graph representations that can capture the structural and functional similarity of sub-structures ( as evidenced by their co-occurrence relationship ) in graphs . This is particularly useful when classifying graphs that have very few sub-structures in common . Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning . We here present a novel method to embed directed acyclic graphs . Recently , there has been a surge of interest in learning representation of graph-structured data that are dynamically evolving . However , current dynamic graph learning methods lack a principled way in modeling temporal , multi-relational , and concurrent interactions between nodes --- a limitation that is especially problematic for the task of temporal knowledge graph reasoning , where the goal is to predict unseen entity relationships ( i.e. , events ) over time . The objective of learning representation of knowledge graph is assumed to encode both entities and relations into a continuous low-dimensional vector space . Previous methods usually represent the same entity in different triples with the same representation . This is followed by a discussion of the relationships between graph kernel methods and neural networks . Next we identify the major approaches used for learning representations of graph data namely : Kernel approaches , Convolutional approaches , Graph neural networks approaches , Graph embedding approaches and Probabilistic approaches . A variety of methods under each of the approaches are discussed and the survey is concluded with a brief discussion of the future of learning representation of graph data . Graph Attention Networks ( GATs ) are the state-of-the-art neural architecture for representation learning with graphs . GATs learn attention functions that assign weights to nodes so that different nodes have different influences in the feature aggregation steps . First , by using a variational probabilistic framework , we introduce priors that encourage source separation , and can use our model 's posterior to predict sentence embeddings for monolingual data at test time . Second , we use high-capacity transformers as both data generating distributions and inference networks -- contrasting with most past work on sentence embeddings . In experiments , our approach substantially outperforms the state-of-the-art on a standard suite of unsupervised semantic similarity evaluations . A set of phrase structure rules and a set of transformational rules are proposed for which the claim is made that these rules enumerate the underlying and derived sentential structures which exemplify two productive classes of sentential embedding in English . These are sentential embedding in noun phrases and sentential embedding in verb phrases . First , following a statement of the grammatical rules , the phrase structure rules are analyzed and defended . To overcome data sparsity problem of short text , sentence representation vector is then derived based on different views from sentence representation matrix . The various views contributes to the construction of an enriched sentence embedding . We employ a residual network on the sentence embedding to get a consistent and refined sentence representation . We present a new unsupervised method for learning general-purpose sentence embeddings . Unlike existing methods which rely on local contexts , such as words inside the sentence or immediately neighboring sentences , our method selects , for each target sentence , influential sentences in the entire document based on a document structure . To build that system , we will explore different state-of-the-art techniques based on neural networks , and more specifically , inspired by neural machine translation recent work . Firstly , we will perform an unsupervised task that focuses in the generation of sentence embeddings ( vectors of real numbers ) representing semantic information in a continuous space . To generate sentence embeddings we will use large corpora , with millions of sentences of of public available books or subtitles of TV series , films and documentaries . This leads to performance rivaling the state of the art on the SICK similarity and entailment tasks . We release all of our resources to the research community with the hope that they can serve as the new baseline for further work on universal sentence embeddings . This paper presents multilingual sentence generation in the GENESE project , part of the efforts to develop pivot-language based machine translation . The handling of individual instances will be developed and the problem of mass expressions will be highlighted . In order to discover latent themes from multilingual data , a standard topic model called Probabilistic Latent Semantic Analysis ( PLSA ) is used in existing literature . However , it addresses the inter-sentence multilingualism . In this paper , we propose a novel method which is basically based on co-occurrences of words within a code-mixed message . This paper presents an extension of a denoising auto-encoder to learn language-independent representations of parallel multilingual sentences . Each sentence from one language is represented using language dependent distributed representations . Information Organization.- Automatic Word Clustering for Text Categorization Using Global Information.- Text Classification Using Web Corpora and EM Algorithms.- Applying CLIR Techniques to Event Tracking.- Document Clustering Using Linear Partitioning Hyperplanes and Reallocation.- Automatic Summarization.- Summary Generation Centered on Important Words.- Sentence Compression Learned by News Headline for Displaying in Small Device.- Automatic Text Summarization Using Two-Step Sentence Extraction.- Sentence Extraction Using Time Features in Multi-document Summarization.- Alignment/Paraphrasing in IR.- Extracting Paraphrases of Japanese Action Word of Sentence Ending Part from Web and Mobile News Articles.- Improving Transliteration with Precise Alignment of Phoneme Chunks and Using Contextual Features.- Combining Sentence Length with Location Information to Align Monolingual Parallel Texts.- Web Search.- Effective Topic Distillation with Key Resource Pre-selection.- Efficient PageRank with Same Out-Link Groups.- Literal-Matching-Biased Link Analysis.- Linguistic Issues in IR.- Multilingual Relevant Sentence Detection Using Reference Corpus.- A Bootstrapping Approach for Geographic Named Entity Annotation.- Using Verb Dependency Matching in a Reading Comprehension System.- Document/Query Models.- Sense Matrix Model and Discrete Cosine Transform.- Query Model Estimations for Relevance Feedback in Language Modeling Approach.- A Measure Based on Optimal Matching in Graph Theory for Document Similarity.- Estimation of Query Model from Parsimonious Translation Model.- Enabling Technology.- Ranking the NTCIR Systems Based on Multigrade Relevance.- X-IOTA : An Open XML Framework for IR Experimentation.- Recognition-Based Digitalization of Korean Historical Archives.- On Bit-Parallel Processing of Multi-byte Text.- Mobile Applications.- Retrieving Regional Information from Web by Contents Localness and User Location.- Towards Understanding the Functions of Web Element.- Clustering-Based Navigation of Image Search Results on Mobile Devices . Multilingual sentence and document representations are becoming increasingly important . We build on recent advances in multilingual sentence encoders , with a focus on efficiency and large-scale applicability . The first is identifying relevant sentences ( categorization ) and the second is identifying new information from those relevant sentences ( novelty mining ) . Many previous studies of relevant sentence retrieval and novelty mining have been conducted on the English language , but few papers have addressed the problem of multilingual sentence categorization and novelty mining . This is an important issue in global business environments , where mining knowledge from text in a single language is not sufficient . We hope that our dataset , dubbed XNLI , will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task . In addition , we provide several baselines for multilingual sentence understanding , including two based on machine translation systems , and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders . We find that XNLI represents a practical and challenging evaluation suite , and that directly translating the test data yields the best performance among available baselines . The thesis employs both theoretical and empirical methods to examine this claim . At the theoretical front , the research makes explicit features of multimodal sentence presentation and of structures and processes involved in multimodal language processing . Two entities are presented : a multimodal design space ( MMDS ) and a multimodal user model ( MMUM ) . document : Margin - based Parallel Corpus Mining with Multilingual Sentence Embeddings Machine translation is highly sensitive to the size and quality of the training data , which has led to an increasing interest in collecting and filtering large parallel corpora .
For this , we present a co-occurrence embedding regularized metric learning model ( CRML ) for collaborative recommendations . We consider the optimization problem as a multi-task learning problem which includes optimizing a primary task of metric learning and two auxiliary tasks of representation learning . In particular , we develop an effective approach for learning the embedding representations of both users and items , and then exploit the strategy of soft parameter sharing to optimize the model parameters .
Current deep clustering methods usually boost the clustering results by means of the powerful representation ability of deep learning , e.g. , autoencoder , suggesting that learning an effective representation for clustering is a crucial requirement . The strength of deep clustering methods is to extract the useful representations from the data itself , rather than the structure of data , which receives scarce attention in representation learning . Motivated by the great success of Graph Convolutional Network ( GCN ) in encoding the graph structure , we propose a Structural Deep Clustering Network ( SDCN ) to integrate the structural information into deep clustering .
A few studies explored utilizing temporal information available in a dynamic setting in order to address the node representation learning . However , the representation learning problem for the entire graph in a dynamic context is yet to be addressed . In this paper , we propose an unsupervised encoder-decoder framework that projects a dynamic graph at each time step into a d-dimensional space , taking into account both the graph 's topology and dynamics .
We focus on metric learning due to the importance of computing distances between examples in learning algorithms and present Jiffy , a simple and scalable distance metric learning method for multivariate time series . Our approach is to reframe the task as a representation learning problem – rather than design an elaborate distance function , we use a CNN to learn an embedding such that the Euclidean distance is effective . Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods .
section : Related Work subsection : Representation Learning from unlabeled data Unsupervised representation learning is a fairly well studied problem in general computer vision research , as well as in the context of images .
As a result , the learned model is biased towards the synthetic data , which leads to an unsatisfactory segmentation performance on real urban scene images . From the perspective of representation learning , this is largely because that the convolutional filters in the semantic segmentation models overfit to the synthetic image style . Consequently , when taking a real image as input , the feature representation generated using the learned model might not be sufficiently informative for semantic segmentation , since the convolutional filters tend to extract information that look discriminative in synthetic style only .
Similarly , it is useful for multiple ( related ) tasks to be learned jointly so that the knowledge learned in one task can benefit other tasks . Recently , there is a growing interest in applying MTL to representation learning using deep neural networks ( DNNs ) collobert2011natural , liu2015mtl , luong2015multi , mt - mrc2018 for two reasons . First , supervised learning of DNNs requires large amounts of task - specific labeled data , which is not always available .
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction , node classification , and graph visualization . Previous methods on graph representation learning mainly focus on static graphs , however , many real-world graphs are dynamic and evolve over time . In this paper , we present Dynamic Self-Attention Network ( DySAT ) , a novel neural architecture that operates on dynamic graphs and learns node representations that capture both structural properties and temporal evolutionary patterns .
Graph Convolutional Networks ( GCNs ) have emerged as the state-of-the-art deep learning model for representation learning on graphs . It is challenging to accelerate training of GCNs , due to ( 1 ) substantial and irregular data communication to propagate information within the graph , and ( 2 ) intensive computation to propagate information along the neural network layers .
Abstract The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces . Mostly current works have demonstrated the benefits of knowledge graph embedding in single knowledge graph completion , such as relation extraction .
We introduce a novel approach to graph-level representation learning , which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity . Our approach , UGRAPHEMB , is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner .
However , these methods fail to capture the co-occurrence relationship between more complex sub-structures in the graph since they were designed primarily for node representation learning . In this work , we study the problem of learning graph representations that can capture the structural and functional similarity of sub-structures ( as evidenced by their co-occurrence relationship ) in graphs . This is particularly useful when classifying graphs that have very few sub-structures in common .
Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning . We here present a novel method to embed directed acyclic graphs .
Recently , there has been a surge of interest in learning representation of graph-structured data that are dynamically evolving . However , current dynamic graph learning methods lack a principled way in modeling temporal , multi-relational , and concurrent interactions between nodes --- a limitation that is especially problematic for the task of temporal knowledge graph reasoning , where the goal is to predict unseen entity relationships ( i.e. , events ) over time .
The objective of learning representation of knowledge graph is assumed to encode both entities and relations into a continuous low-dimensional vector space . Previous methods usually represent the same entity in different triples with the same representation .
This is followed by a discussion of the relationships between graph kernel methods and neural networks . Next we identify the major approaches used for learning representations of graph data namely : Kernel approaches , Convolutional approaches , Graph neural networks approaches , Graph embedding approaches and Probabilistic approaches . A variety of methods under each of the approaches are discussed and the survey is concluded with a brief discussion of the future of learning representation of graph data .
Graph Attention Networks ( GATs ) are the state-of-the-art neural architecture for representation learning with graphs . GATs learn attention functions that assign weights to nodes so that different nodes have different influences in the feature aggregation steps .
First , by using a variational probabilistic framework , we introduce priors that encourage source separation , and can use our model 's posterior to predict sentence embeddings for monolingual data at test time . Second , we use high-capacity transformers as both data generating distributions and inference networks -- contrasting with most past work on sentence embeddings . In experiments , our approach substantially outperforms the state-of-the-art on a standard suite of unsupervised semantic similarity evaluations .
A set of phrase structure rules and a set of transformational rules are proposed for which the claim is made that these rules enumerate the underlying and derived sentential structures which exemplify two productive classes of sentential embedding in English . These are sentential embedding in noun phrases and sentential embedding in verb phrases . First , following a statement of the grammatical rules , the phrase structure rules are analyzed and defended .
To overcome data sparsity problem of short text , sentence representation vector is then derived based on different views from sentence representation matrix . The various views contributes to the construction of an enriched sentence embedding . We employ a residual network on the sentence embedding to get a consistent and refined sentence representation .
We present a new unsupervised method for learning general-purpose sentence embeddings . Unlike existing methods which rely on local contexts , such as words inside the sentence or immediately neighboring sentences , our method selects , for each target sentence , influential sentences in the entire document based on a document structure .
To build that system , we will explore different state-of-the-art techniques based on neural networks , and more specifically , inspired by neural machine translation recent work . Firstly , we will perform an unsupervised task that focuses in the generation of sentence embeddings ( vectors of real numbers ) representing semantic information in a continuous space . To generate sentence embeddings we will use large corpora , with millions of sentences of of public available books or subtitles of TV series , films and documentaries .
This leads to performance rivaling the state of the art on the SICK similarity and entailment tasks . We release all of our resources to the research community with the hope that they can serve as the new baseline for further work on universal sentence embeddings .
This paper presents multilingual sentence generation in the GENESE project , part of the efforts to develop pivot-language based machine translation . The handling of individual instances will be developed and the problem of mass expressions will be highlighted .
In order to discover latent themes from multilingual data , a standard topic model called Probabilistic Latent Semantic Analysis ( PLSA ) is used in existing literature . However , it addresses the inter-sentence multilingualism . In this paper , we propose a novel method which is basically based on co-occurrences of words within a code-mixed message .
This paper presents an extension of a denoising auto-encoder to learn language-independent representations of parallel multilingual sentences . Each sentence from one language is represented using language dependent distributed representations .
Information Organization.- Automatic Word Clustering for Text Categorization Using Global Information.- Text Classification Using Web Corpora and EM Algorithms.- Applying CLIR Techniques to Event Tracking.- Document Clustering Using Linear Partitioning Hyperplanes and Reallocation.- Automatic Summarization.- Summary Generation Centered on Important Words.- Sentence Compression Learned by News Headline for Displaying in Small Device.- Automatic Text Summarization Using Two-Step Sentence Extraction.- Sentence Extraction Using Time Features in Multi-document Summarization.- Alignment/Paraphrasing in IR.- Extracting Paraphrases of Japanese Action Word of Sentence Ending Part from Web and Mobile News Articles.- Improving Transliteration with Precise Alignment of Phoneme Chunks and Using Contextual Features.- Combining Sentence Length with Location Information to Align Monolingual Parallel Texts.- Web Search.- Effective Topic Distillation with Key Resource Pre-selection.- Efficient PageRank with Same Out-Link Groups.- Literal-Matching-Biased Link Analysis.- Linguistic Issues in IR.- Multilingual Relevant Sentence Detection Using Reference Corpus.- A Bootstrapping Approach for Geographic Named Entity Annotation.- Using Verb Dependency Matching in a Reading Comprehension System.- Document/Query Models.- Sense Matrix Model and Discrete Cosine Transform.- Query Model Estimations for Relevance Feedback in Language Modeling Approach.- A Measure Based on Optimal Matching in Graph Theory for Document Similarity.- Estimation of Query Model from Parsimonious Translation Model.- Enabling Technology.- Ranking the NTCIR Systems Based on Multigrade Relevance.- X-IOTA : An Open XML Framework for IR Experimentation.- Recognition-Based Digitalization of Korean Historical Archives.- On Bit-Parallel Processing of Multi-byte Text.- Mobile Applications.- Retrieving Regional Information from Web by Contents Localness and User Location.- Towards Understanding the Functions of Web Element.- Clustering-Based Navigation of Image Search Results on Mobile Devices .
Multilingual sentence and document representations are becoming increasingly important . We build on recent advances in multilingual sentence encoders , with a focus on efficiency and large-scale applicability .
The first is identifying relevant sentences ( categorization ) and the second is identifying new information from those relevant sentences ( novelty mining ) . Many previous studies of relevant sentence retrieval and novelty mining have been conducted on the English language , but few papers have addressed the problem of multilingual sentence categorization and novelty mining . This is an important issue in global business environments , where mining knowledge from text in a single language is not sufficient .
We hope that our dataset , dubbed XNLI , will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task . In addition , we provide several baselines for multilingual sentence understanding , including two based on machine translation systems , and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders . We find that XNLI represents a practical and challenging evaluation suite , and that directly translating the test data yields the best performance among available baselines .
The thesis employs both theoretical and empirical methods to examine this claim . At the theoretical front , the research makes explicit features of multimodal sentence presentation and of structures and processes involved in multimodal language processing . Two entities are presented : a multimodal design space ( MMDS ) and a multimodal user model ( MMUM ) .
document : Margin - based Parallel Corpus Mining with Multilingual Sentence Embeddings Machine translation is highly sensitive to the size and quality of the training data , which has led to an increasing interest in collecting and filtering large parallel corpora .
Effective metric learning with co-occurrence embedding for collaborative recommendations Structural Deep Clustering Network Predictive Temporal Embedding of Dynamic Graphs A tale of two time series methods : representationlearning for improved distance and risk metrics; Representation learning for improved distance and riskmetrics Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Multi-Task Deep Neural Networks for Natural Language Understanding Dynamic Graph Representation Learning via Self-Attention Networks GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms Representation learning over multiple knowledge graphs for knowledge graphs alignment Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity Learning compact graph representations via an encoder-decoder network Hyperbolic Entailment Cones for Learning Hierarchical Embeddings Recurrent Event Network for Reasoning over Temporal Knowledge Graphs Knowledge Graph Completion via Complete Attention between Knowledge Graph and Entity Descriptions Learning Representations of Graph Data - A Survey Improving Graph Attention Networks with Large Margin-based Constraints A Bilingual Generative Transformer for Semantic Sentence Embedding The grammar of English predicate complement constructions Character-aware Attention Residual Network for Sentence Representation Unsupervised Sentence Embedding Using Document Structure-Based Context Neural Paraphrasing Generation System Towards Universal Paraphrastic Sentence Embeddings The Realization of Individual Instances in a Multilingual Generation System Discovering thematic knowledge from code-mixed chat messages using topic model Learning language-independent sentence representations for multi-lingual, multi-document summarization Information Retrieval Technology, Asia Information Retrieval Symposium, AIRS 2004, Beijing, China, October 18-20, 2004, Revised Selected Papers Analysis of Joint Multilingual Sentence Representations and Semantic K-Nearest Neighbor Graphs Multilingual sentence categorization and novelty mining XNLI: Evaluating Cross-lingual Sentence Representations Integrating speech and visual text in multimodal interfaces Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
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WWW ASONAM ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ACL FPGA CSAE ECML/PKDD (2) AAAI EMNLP ACL
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