Network embedding is a promising topic that maps the vertices to the latent space while keeps the structural proximity in the original space . The network embedding task is difficult since the network vertices have no specific time or space orders . Models that used to extract information from images and texts with regular space or time structures can not be directly applied in network heading . A key step lies in mapping a virtual network request to a resource allocation in the network substrate . Previous approaches to this network embedding problem assumed the request will ask for specific resources , such as network capacity or computing power . However , the end-user is more interested in performance . Learning network embedding for large-scale networks have been attracting increasing attention due to their importance in supporting numerous network analytic and data mining tasks such as node classification , clustering and visualization . In this paper , we present a novel framework for learning large-scale network embedding incorporating network topology and community structural information . In this paper , we present our random walkbased multilayer network embedding and compare it with single layerand multilayer network embeddings . For this purpose , we used severalclassic datasets usually used in network embedding experiments and alsocollected our own dataset of papers and authors indexed in Scopus . Attention operators have been widely applied in various fields , including computer vision , natural language processing , and network embedding learning . Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes . Further , we build a taxonomy of network embedding methods based on the type of networks and review the major research works that come under each category . We then cover the major datasets used in network embedding research and describe the major applications of network embedding with respect to various network mining tasks . Finally , we provide various directions for future work which enhance further research . Capturing neighborhood information by generating node sequences or node samples is an important prerequisite step for many of the neural network embedding approaches . Majority of the recent studies on neural network embedding exploit random walk as a sampling method , which traverses through adjacent neighbors to generate the node sequences . Traversing through only immediate neighbor may not be suitable particularly for heterogeneous information networks ( HIN ) where adjacent nodes tend to belong to different types . For example , the time complexity of classical graph embedding algorithms such as MDS , IsoMap , Laplacian eigenmap are at least quadratic to the number of vertices , which is too expensive for networks with millions of nodes . Although a few very recent studies approach the embedding of large - scale networks , these methods either use an indirect approach that is not designed for networks ( e.g. , ) or lack a clear objective function tailored for network embedding ( e.g. , ) . We anticipate that a new model with a carefully designed objective function that preserves properties of the graph and an efficient optimization technique should effectively find the embedding of millions of nodes . Practically , the graph factorization method only applies to undirected graphs while the proposed model is applicable for both undirected and directed graphs . The most recent work related with ours is DeepWalk , which deploys a truncated random walk for social network embedding . Although empirically effective , the DeepWalk does not provide a clear objective that articulates what network properties are preserved . Reference code and data are available at . network embedding , link prediction , semi - supervised learning , multi - task learning section : Introduction This concatenated vector is fed into a fully connected layer with nonlinear activation function , which updates the forward representation of , h⊢vk , to be used at the next iteration ; We update the backward representation of , h⊣vk , using the similar procedure as introduced in step ( 3 ) and ( 4 ) except that operating on the backward representations instead of the forward representations ; We repeat steps ( 3 ) ( 5 ) times , and the concatenation of the final forward and backward representation is used as the final bi - directional representation of . Since the neighbor information from different hops may have different impact on the node embedding , we learn a distinct aggregator at each iteration . Aggregator Architectures . While recent work has produced artificial agents proficient in abstract games , the environments these agents act within are far removed the real world and thus these agents provide little insight into the advantages of embodied play . Hiding games have arisen in multiple cultures and species , and provide a rich ground for studying the impact of embodied gameplay on representation learning in the context of perspective taking , secret keeping , and false belief understanding . Here we are the first to show that embodied adversarial reinforcement learning agents playing cache , a variant of hide-and-seek , in a high fidelity , interactive , environment , learn representations of their observations encoding information such as occlusion , object permanence , free space , and containment ; on par with representations learnt by the most popular modern paradigm for visual representation learning which requires large datasets independently labeled for each new task . Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning . However , most existing models ignore to represent hierarchical knowledge , such as the similarities and dissimilarities of entities in one domain . We present Jiffy , a simple and scalable distance metric 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 . By aggressively max-pooling and downsampling , we are able to construct this embedding using a highly compact neural network . We argue that the implicit and the explicit mapping from a higher-dimensional to a lower-dimensional vector space is the key to learn more useful , highly predictable , and gracefully interpretable representations . Having good interpretable representations provides a good guidance to understand how each view contributes to the representation learning process . In addition , it helps us to exclude unrelated dimensions . where wi ∈ V ( V is the vocabulary ) , we would like to maximize the Pr ( wn|w0 , w1 , · · · , wn−1 ) over all the training corpus . Recent work in representation learning has focused on using probabilistic neural networks to build general representations of words which extend the scope of language modeling beyond its original goals . In this work , we present a generalization of language modeling to explore the graph through a stream of short random walks . section : Conclusions In this paper , SVDNet is proposed for representation learning in pedestrian retrieval , or re - identification . Decorrelation is enforced among the projection vectors in the weight matrix of the FC layer . section : Introduction The domain of representation learning has undergone tremendous advances due to improved supervised learning techniques . However , unsupervised learning has the potential to leverage large pools of unlabeled data , and extend these advances to modalities that are otherwise impractical or impossible . document : The Variational Fair Autoencoder We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible . Our model is based on a variational autoencoding architecture kingma2013auto , rezende2014stochastic with priors that encourage independence between sensitive and latent factors of variation . section : Future Directions The last few years have seen tremendous advances in learning representations for reinforcement learning . Surprisingly , these advances have yet to carry over to the problem of exploration . document : Multi - Task Deep Neural Networks for Natural Language Understanding In this paper , we present a Multi - Task Deep Neural Network ( MT - DNN ) for learning representations across multiple natural language understanding ( NLU ) tasks . MT - DNN not only leverages large amounts of cross - task data , but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains . In addition to demonstrating that these four augmented ring networks are grid graphs , our embedding results afford us close bounds on how much decrease in diameter is achievable for a given increase in structural complexity for the networks . Specifically , we derive upper and lower bounds on the optimal diameters of N-node depth-d multirings and HRNs that are asymptotically tight for large N and d. Index TermsÐRing networks , chordal rings , express rings , multirings , hierarchical ring networks , grid graphs , graph embedding , diameter trade-offs . The kernel function can be naturally defined according to the signal strength matrix . Then we formulate the localization problem as a graph embedding problem . Finally , we use the kernel locality preserving projection ( KLPP ) technique to estimate the relative locations of all sensor nodes . In graph embedding based methods , we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm ( e.g. LLE ) . It is difficult and ... In graph embedding based methods , we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm ( e.g. LLE ) . It is difficult and unstable to manually choose the nearest neighbors in high dimensional space . 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 . The learned neural network can be considered as a function that receives any graph as input , either seen or unseen in the training set , and transforms it into an embedding . The model can be efficiently trained and scalable to large knowledge graphs . However , there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network ( GCN ) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure . In this work , we propose a novel end-to-end StructureAware Convolutional Network ( SACN ) that takes the benefit of GCN and ConvE together . Abstract We show that , in general , graph embeddability in pseudosurfaces can not be characterized by means of a finite set of forbidden ( = irreducible ) graphs . THIS PAPER COMBINES THE RESEARCH TOPICS OF TWO FAMILIES OF INVESTIGA- TIONS THAT HAVE APPEARED IN THE LITERATURE IN RECENT YEARS . THE FIRST TYPE OF INVESTIGATION EXPANDS THE OBJECT OF STUDY IN GRAPH- EMBEDDING RESEARCH FROM GRAPHS TO HYPERGRAPHS , MOTIVATED BY THE POPULARITY OF " BUS-ORIENTED " ARCHITECTURES IN PRESENT-DAY MICROELECTRONICS . paragraph : Relation Embeddings In the most recent years graph embedding has been widely employed to learn representation for vertexes and their relations in a graph . In our work here , we also capture the relation between any two words in WordNet through relation embedding . These representation learning approaches can be roughly categorized into two classes including matrix factorization - based algorithms and random - walk based methods . A line of research learn the embeddings of graph nodes through matrix factorization roweis2000nonlinear , belkin2002laplacian , ahmed2013distributed , cao2015grarep , ou2016asymmetric . These methods directly train embeddings for individual nodes of training and testing data jointly and thus inherently transductive . By applying max - pooling , the model can capture different information across the neighborhood set . subsection : Graph Embedding Generation Most existing works of graph convolution neural networks focus more on node embeddings rather than graph embeddings since their focus is on the node - wise classification task . The last states of bidirectional - LSTM on query are concatenated to be the initial internal state We apply another bidirectional - LSTM layer with 128 and 256 cells on graph description embeddings in the small and large graph datasets , respectively . It maps each symbol i to a contextual representation given by the concatenation of forward and backward LSTM hidden states subsection : Learning Embeddings Extensive research was done on learning graph embeddings . A probabilistic generative model was proposed to learn node embeddings that generate the edges in a graph . Among the most recent literature is a technique called graph factorization . It finds the low - dimensional embedding of a large graph through matrix factorization , which is optimized using stochastic gradient descent . This is possible because a graph can be represented as an affinity matrix . The experimental results of semantic reasoning and word embedding visualization show that CWDR-BiGRU can effectively train bilingual word embeddings . In the task of Chinese-Tibetan cross-lingual document classification , the accuracy of transfer learning method based on CWDR-BiGRU is higher than the conventional method by 13.5 % , and higher than the existing Bilingual Autoencoder , BilBOWA , BiCCV and BiSkip by 7.4 % , 5.8 % , 3.1 % and 1.6 % respectively , indicating CWDR-BiGRU which has reduced the difficulty of acquiring corpora for bilingual word embeddings can accurately excavate the deep alignment relationship and semantic properties . We introduce Earth Mover 's Distance to this task by providing a natural formulation that translates words in a holistic fashion , addressing the limitations of the nearest neighbor . We further extend the formulation to a new task of identifying parallel sentences , which is useful for statistical machine translation systems , thereby expanding the application realm of bilingual word embeddings . We show encouraging performance on both tasks . In the cross-lingual setup , we assume there is access to annotated resources as well as a well trained model in the source language and little to no annotated data in the target language . In this paper , we explore three different approaches for cross-lingual transfer \textemdash~\ delexicalization as data augmentation , multilingual embeddings and machine translation . We compare these approaches both on a low resource setting as well as a large resource setting . In the present paper , we investigate the efficacy of multilingual embeddings learned from weakly-supervised image-text data . In particular , we propose methods for learning multilingual embeddings using image-text data , by enforcing similarity between the representations of the image and that of the text . Our experiments reveal that even without using any expensive labeled data , a bag-of-words-based embedding model trained on image-text data achieves performance comparable to the state-of-the-art on crosslingual semantic similarity tasks . The sum-of-correlations ( SUMCOR ) generalized canonical correlation analysis ( GCCA ) aims at producing low-dimensional representations of multiview data via enforcing pairwise similarity of the reduced-dimension views . SUMCOR has been applied to a large variety of applications including blind separation , multilingual word embedding , and cross-modality retrieval . Despite the NP-hardness of SUMCOR , recent work has proposed effective algorithms for handling it at very large scale . Our results show that while CVM and CCA based embeddings perform as well as the proposed embedding technique on semantic and syntactic tasks respectively , the proposed approach provides the best performance for both tasks overall . Thus , this study demonstrates that existing bilingual embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embedding from the code-mixed text . Our experiments show the effectiveness of the proposed models , achieving promising results over a strong baseline . At the same time , our work makes important contributions in relation to bilingual word embedding mappings and word embedding based phrase similarity measures , which go beyond machine translation and have an intrinsic value in the field of distributional semantics . Language Analysis and Processing Distributional Semantics and Machine Learning for SMT iv/112 Language Analysis and Processing Distributional Semantics and Machine Learning for SMT v/112 Neighborhood Sensitive Maps for Multilingual Word Embeddings . Inducing multilingual word embeddings by learning a linear map between embedding spaces of different languages achieves remarkable accuracy on related languages . However , accuracy drops substantially when translating between distant languages . In this paper , we explore more extensively and systematically two flavors of the CLTC problem : news topic classification and textual churn intent detection ( TCID ) in social media . In particular , we test the hypothesis that embeddings with context are more effective , by multi-tasking the learning of multilingual word embeddings and text classification ; we explore neural architectures for CLTC ; and we move from bi- to multi-lingual word embeddings . For all architectures , types of word embeddings and datasets , we notice a consistent gain trend in favor of multilingual joint training , especially for low-resourced languages . We use the original ( monolingual ) model to generate sentence embeddings for the source language and then train a new system on translated sentences to mimic the original model . Compared to other methods for training multilingual sentence embeddings , this approach has several advantages : It is easy to extend existing models with relatively few samples to new languages , it is easier to ensure desired properties for the vector space , and the hardware requirements for training is lower . We demonstrate the effectiveness of our approach for 10 languages from various language families . We argue that minor senses play an important role in word similarity computations , hence we use an unsupervised sense inventory resource to retrofit monolingual word embeddings , producing sense-aware embeddings . Using retrofitted sense-aware embeddings , we show improved word similarity and relatedness results on multiple word embeddings and multiple established word similarity tasks , sometimes up to an impressive margin of + 0.15 Spearman correlation score . However , it has also been shown that for distant language pairs such fully unsupervised self-learning methods are unstable and often get stuck in poor local optima due to reduced isomorphism between starting monolingual spaces . In this work , we propose a new robust framework for learning unsupervised multilingual word embeddings that mitigates the instability issues . We learn a shared multilingual embedding space for a variable number of languages by incrementally adding new languages one by one to the current multilingual space . document : Massively Multilingual Sentence Embeddings for Zero - Shot Cross - Lingual Transfer and Beyond We introduce an architecture to learn joint multilingual sentence representations for 93 languages , belonging to more than 30 different language families and written in 28 different scripts . section : Related work Following the success of word embeddings mikolov2013distributed , pennington2014glove , there has been an increasing interest in learning continuous vector representations of longer linguistic units like sentences . These sentence embeddings are commonly obtained using a Recurrent Neural Network ( RNN ) encoder , which is typically trained in an unsupervised way over large collections of unlabelled corpora . While the previous methods consider a single language at a time , multilingual representations have attracted a large attention in recent times . Most of this research focuses on cross - lingual word embeddings ruder2017survey , which are commonly learned jointly from parallel corpora gouws2015bilbowa , luong2015bilingual . An alternative approach that is becoming increasingly popular is to train word embeddings independently for each language over monolingual corpora , and then map them to a shared space based on a bilingual dictionary mikolov2013exploiting , artetxe2018generalizing or even in a fully unsupervised manner . It can be seen that reshaping the embeddings is of no benefit , especially on WN18RR . These results indicate that the purpose of convolution on word embeddings is not to find patterns in a 2D embedding ( as with images ) , but perhaps to limit the number of dimensions that can interact with each other , thereby avoiding overfitting . Label Smoothing Contrary to the ablation study of dettmers2017convolutional , showing the influence of hyperparameters on mean reciprocal rank for FB15k - 237 , from which they deem label smoothing unimportant , we find label smoothing to give a significant improvement in prediction scores for WN18RR . While the character component learns general regularities that are shared between all the words , individual word embeddings provide a way for the model to store word - specific information and any exceptions . Therefore , while we want the character - based model to shift towards predicting high - quality word embeddings , it is not desireable to optimise the word embeddings towards the character - level representations . This can be achieved by making sure that the optimisation is performed only in one direction ; in Theano , the disconnected_grad function gives the desired effect . Kim2016 implemented a language model where encoding is performed by a convolutional network and LSTM over characters , whereas predictions are given on the word - level . Cao2016 proposed a method for learning both word embeddings and morphological segmentation with a bidirectional recurrent network over characters . There is also research on performing parsing and text classification with character - level neural models . 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 . In this paper , we propose a new method for this task based on multilingual sentence embeddings . Our approach uses an encoder - decoder trained over an initial parallel corpus to build multilingual sentence representations , which are then incorporated into a new margin - based method to score , mine and filter parallel sentences . In comparison , our approach is conceptually simpler , and obtains substantially better results ( e.g. 48.9 vs 83.3 P@1 on English - French UN reconstruction ) . section : Multilingual sentence embeddings Following recent trend , we use a sequence - to - sequence encoder - decoder to learn our sentence embeddings Schwenk:2017:repl4nlp , espana2017empirical , hassan2018achieving , schwenk2018filtering . CNN has been very successful on image classification ; see e.g. , the winning solutions of ImageNet Large Scale Visual Recognition Challenge . On text , since the work on token - level applications ( e.g. , POS tagging ) by nnnlpJMLR11 , CNN has been used in systems for entity search , sentence modeling , word embedding learning , product feature mining , and so on . Notably , in many of these CNN studies on text , the first layer of the network converts words in sentences to word vectors by table lookup . We observe that the quality of learned word embedding distribution keeps improving as goes up from 0 to 0.1 , but starts to drop around . We also find that this optimal in word embedding learning ( i.e. , 0.1 ) is larger than the which yielded the best tagging performance on development sets ( i.e. , 0.01 or 0.05 ) . A possible explanation is that while word embeddings can adapt to relatively large ( e.g. , 0.1 ) during training , as adversarial perturbations are generated at the embedding level , such could change the semantics of the input from the current tagging model ’s perspective and hinder the training of tagging . Moreover , the fact that the two components are separately trained can limit its expressiveness . levy2014neural also considered the matrix factorization perspective , but in the context of learning word embeddings . In a general sense , Mixture of Softmaxes proposed in this work can be seen as a particular instantiation of the long - existing idea called Mixture of Experts ( MoE ) jacobs1991adaptive . Early approaches to deep semi - supervised learning pre - train neural models on unlabeled data , which has been successful for applications in computer vision jarrett2009best , lecun2010convolutional and NLP . Particularly noteworthy for NLP are algorithms for learning effective word embeddings and language model pretraining dai2015semi , ramachandran2016unsupervised , peters2018deep , howard2018universal , radford2018improving . Pre - training on other tasks such as machine translation has also been studied McCann2017LearnedIT . Specifically , we first input one - hot - encoding character sequences to a convolution layer to enhance the semantic nonlinear representation ability of our model , and the output is then fed into a multi - gram ( i.e. different window sizes ) convolution layer to capture different local character chunk information . For word - level embedding , we use pre - trained word vectors , GloVe , to map each word to a low - dimensional vector space . Finally , each word is represented as a concatenation of the character - level embedding and word - level embedding .
Network embedding is a promising topic that maps the vertices to the latent space while keeps the structural proximity in the original space . The network embedding task is difficult since the network vertices have no specific time or space orders . Models that used to extract information from images and texts with regular space or time structures can not be directly applied in network heading .
A key step lies in mapping a virtual network request to a resource allocation in the network substrate . Previous approaches to this network embedding problem assumed the request will ask for specific resources , such as network capacity or computing power . However , the end-user is more interested in performance .
Learning network embedding for large-scale networks have been attracting increasing attention due to their importance in supporting numerous network analytic and data mining tasks such as node classification , clustering and visualization . In this paper , we present a novel framework for learning large-scale network embedding incorporating network topology and community structural information .
In this paper , we present our random walkbased multilayer network embedding and compare it with single layerand multilayer network embeddings . For this purpose , we used severalclassic datasets usually used in network embedding experiments and alsocollected our own dataset of papers and authors indexed in Scopus .
Attention operators have been widely applied in various fields , including computer vision , natural language processing , and network embedding learning . Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes .
Further , we build a taxonomy of network embedding methods based on the type of networks and review the major research works that come under each category . We then cover the major datasets used in network embedding research and describe the major applications of network embedding with respect to various network mining tasks . Finally , we provide various directions for future work which enhance further research .
Capturing neighborhood information by generating node sequences or node samples is an important prerequisite step for many of the neural network embedding approaches . Majority of the recent studies on neural network embedding exploit random walk as a sampling method , which traverses through adjacent neighbors to generate the node sequences . Traversing through only immediate neighbor may not be suitable particularly for heterogeneous information networks ( HIN ) where adjacent nodes tend to belong to different types .
For example , the time complexity of classical graph embedding algorithms such as MDS , IsoMap , Laplacian eigenmap are at least quadratic to the number of vertices , which is too expensive for networks with millions of nodes . Although a few very recent studies approach the embedding of large - scale networks , these methods either use an indirect approach that is not designed for networks ( e.g. , ) or lack a clear objective function tailored for network embedding ( e.g. , ) . We anticipate that a new model with a carefully designed objective function that preserves properties of the graph and an efficient optimization technique should effectively find the embedding of millions of nodes .
Practically , the graph factorization method only applies to undirected graphs while the proposed model is applicable for both undirected and directed graphs . The most recent work related with ours is DeepWalk , which deploys a truncated random walk for social network embedding . Although empirically effective , the DeepWalk does not provide a clear objective that articulates what network properties are preserved .
Reference code and data are available at . network embedding , link prediction , semi - supervised learning , multi - task learning section : Introduction
This concatenated vector is fed into a fully connected layer with nonlinear activation function , which updates the forward representation of , h⊢vk , to be used at the next iteration ; We update the backward representation of , h⊣vk , using the similar procedure as introduced in step ( 3 ) and ( 4 ) except that operating on the backward representations instead of the forward representations ; We repeat steps ( 3 ) ( 5 ) times , and the concatenation of the final forward and backward representation is used as the final bi - directional representation of . Since the neighbor information from different hops may have different impact on the node embedding , we learn a distinct aggregator at each iteration . Aggregator Architectures .
While recent work has produced artificial agents proficient in abstract games , the environments these agents act within are far removed the real world and thus these agents provide little insight into the advantages of embodied play . Hiding games have arisen in multiple cultures and species , and provide a rich ground for studying the impact of embodied gameplay on representation learning in the context of perspective taking , secret keeping , and false belief understanding . Here we are the first to show that embodied adversarial reinforcement learning agents playing cache , a variant of hide-and-seek , in a high fidelity , interactive , environment , learn representations of their observations encoding information such as occlusion , object permanence , free space , and containment ; on par with representations learnt by the most popular modern paradigm for visual representation learning which requires large datasets independently labeled for each new task .
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning . However , most existing models ignore to represent hierarchical knowledge , such as the similarities and dissimilarities of entities in one domain .
We present Jiffy , a simple and scalable distance metric 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 . By aggressively max-pooling and downsampling , we are able to construct this embedding using a highly compact neural network .
We argue that the implicit and the explicit mapping from a higher-dimensional to a lower-dimensional vector space is the key to learn more useful , highly predictable , and gracefully interpretable representations . Having good interpretable representations provides a good guidance to understand how each view contributes to the representation learning process . In addition , it helps us to exclude unrelated dimensions .
where wi ∈ V ( V is the vocabulary ) , we would like to maximize the Pr ( wn|w0 , w1 , · · · , wn−1 ) over all the training corpus . Recent work in representation learning has focused on using probabilistic neural networks to build general representations of words which extend the scope of language modeling beyond its original goals . In this work , we present a generalization of language modeling to explore the graph through a stream of short random walks .
section : Conclusions In this paper , SVDNet is proposed for representation learning in pedestrian retrieval , or re - identification . Decorrelation is enforced among the projection vectors in the weight matrix of the FC layer .
section : Introduction The domain of representation learning has undergone tremendous advances due to improved supervised learning techniques . However , unsupervised learning has the potential to leverage large pools of unlabeled data , and extend these advances to modalities that are otherwise impractical or impossible .
document : The Variational Fair Autoencoder We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible . Our model is based on a variational autoencoding architecture kingma2013auto , rezende2014stochastic with priors that encourage independence between sensitive and latent factors of variation .
section : Future Directions The last few years have seen tremendous advances in learning representations for reinforcement learning . Surprisingly , these advances have yet to carry over to the problem of exploration .
document : Multi - Task Deep Neural Networks for Natural Language Understanding In this paper , we present a Multi - Task Deep Neural Network ( MT - DNN ) for learning representations across multiple natural language understanding ( NLU ) tasks . MT - DNN not only leverages large amounts of cross - task data , but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains .
In addition to demonstrating that these four augmented ring networks are grid graphs , our embedding results afford us close bounds on how much decrease in diameter is achievable for a given increase in structural complexity for the networks . Specifically , we derive upper and lower bounds on the optimal diameters of N-node depth-d multirings and HRNs that are asymptotically tight for large N and d. Index TermsÐRing networks , chordal rings , express rings , multirings , hierarchical ring networks , grid graphs , graph embedding , diameter trade-offs .
The kernel function can be naturally defined according to the signal strength matrix . Then we formulate the localization problem as a graph embedding problem . Finally , we use the kernel locality preserving projection ( KLPP ) technique to estimate the relative locations of all sensor nodes .
In graph embedding based methods , we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm ( e.g. LLE ) . It is difficult and ...
In graph embedding based methods , we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm ( e.g. LLE ) . It is difficult and unstable to manually choose the nearest neighbors in high dimensional space .
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 . The learned neural network can be considered as a function that receives any graph as input , either seen or unseen in the training set , and transforms it into an embedding .
The model can be efficiently trained and scalable to large knowledge graphs . However , there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network ( GCN ) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure . In this work , we propose a novel end-to-end StructureAware Convolutional Network ( SACN ) that takes the benefit of GCN and ConvE together .
Abstract We show that , in general , graph embeddability in pseudosurfaces can not be characterized by means of a finite set of forbidden ( = irreducible ) graphs .
THIS PAPER COMBINES THE RESEARCH TOPICS OF TWO FAMILIES OF INVESTIGA- TIONS THAT HAVE APPEARED IN THE LITERATURE IN RECENT YEARS . THE FIRST TYPE OF INVESTIGATION EXPANDS THE OBJECT OF STUDY IN GRAPH- EMBEDDING RESEARCH FROM GRAPHS TO HYPERGRAPHS , MOTIVATED BY THE POPULARITY OF " BUS-ORIENTED " ARCHITECTURES IN PRESENT-DAY MICROELECTRONICS .
paragraph : Relation Embeddings In the most recent years graph embedding has been widely employed to learn representation for vertexes and their relations in a graph . In our work here , we also capture the relation between any two words in WordNet through relation embedding .
These representation learning approaches can be roughly categorized into two classes including matrix factorization - based algorithms and random - walk based methods . A line of research learn the embeddings of graph nodes through matrix factorization roweis2000nonlinear , belkin2002laplacian , ahmed2013distributed , cao2015grarep , ou2016asymmetric . These methods directly train embeddings for individual nodes of training and testing data jointly and thus inherently transductive .
By applying max - pooling , the model can capture different information across the neighborhood set . subsection : Graph Embedding Generation Most existing works of graph convolution neural networks focus more on node embeddings rather than graph embeddings since their focus is on the node - wise classification task .
The last states of bidirectional - LSTM on query are concatenated to be the initial internal state We apply another bidirectional - LSTM layer with 128 and 256 cells on graph description embeddings in the small and large graph datasets , respectively . It maps each symbol i to a contextual representation given by the concatenation of forward and backward LSTM hidden states
subsection : Learning Embeddings Extensive research was done on learning graph embeddings . A probabilistic generative model was proposed to learn node embeddings that generate the edges in a graph .
Among the most recent literature is a technique called graph factorization . It finds the low - dimensional embedding of a large graph through matrix factorization , which is optimized using stochastic gradient descent . This is possible because a graph can be represented as an affinity matrix .
The experimental results of semantic reasoning and word embedding visualization show that CWDR-BiGRU can effectively train bilingual word embeddings . In the task of Chinese-Tibetan cross-lingual document classification , the accuracy of transfer learning method based on CWDR-BiGRU is higher than the conventional method by 13.5 % , and higher than the existing Bilingual Autoencoder , BilBOWA , BiCCV and BiSkip by 7.4 % , 5.8 % , 3.1 % and 1.6 % respectively , indicating CWDR-BiGRU which has reduced the difficulty of acquiring corpora for bilingual word embeddings can accurately excavate the deep alignment relationship and semantic properties .
We introduce Earth Mover 's Distance to this task by providing a natural formulation that translates words in a holistic fashion , addressing the limitations of the nearest neighbor . We further extend the formulation to a new task of identifying parallel sentences , which is useful for statistical machine translation systems , thereby expanding the application realm of bilingual word embeddings . We show encouraging performance on both tasks .
In the cross-lingual setup , we assume there is access to annotated resources as well as a well trained model in the source language and little to no annotated data in the target language . In this paper , we explore three different approaches for cross-lingual transfer \textemdash~\ delexicalization as data augmentation , multilingual embeddings and machine translation . We compare these approaches both on a low resource setting as well as a large resource setting .
In the present paper , we investigate the efficacy of multilingual embeddings learned from weakly-supervised image-text data . In particular , we propose methods for learning multilingual embeddings using image-text data , by enforcing similarity between the representations of the image and that of the text . Our experiments reveal that even without using any expensive labeled data , a bag-of-words-based embedding model trained on image-text data achieves performance comparable to the state-of-the-art on crosslingual semantic similarity tasks .
The sum-of-correlations ( SUMCOR ) generalized canonical correlation analysis ( GCCA ) aims at producing low-dimensional representations of multiview data via enforcing pairwise similarity of the reduced-dimension views . SUMCOR has been applied to a large variety of applications including blind separation , multilingual word embedding , and cross-modality retrieval . Despite the NP-hardness of SUMCOR , recent work has proposed effective algorithms for handling it at very large scale .
Our results show that while CVM and CCA based embeddings perform as well as the proposed embedding technique on semantic and syntactic tasks respectively , the proposed approach provides the best performance for both tasks overall . Thus , this study demonstrates that existing bilingual embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embedding from the code-mixed text .
Our experiments show the effectiveness of the proposed models , achieving promising results over a strong baseline . At the same time , our work makes important contributions in relation to bilingual word embedding mappings and word embedding based phrase similarity measures , which go beyond machine translation and have an intrinsic value in the field of distributional semantics . Language Analysis and Processing Distributional Semantics and Machine Learning for SMT iv/112 Language Analysis and Processing Distributional Semantics and Machine Learning for SMT v/112
Neighborhood Sensitive Maps for Multilingual Word Embeddings . Inducing multilingual word embeddings by learning a linear map between embedding spaces of different languages achieves remarkable accuracy on related languages . However , accuracy drops substantially when translating between distant languages .
In this paper , we explore more extensively and systematically two flavors of the CLTC problem : news topic classification and textual churn intent detection ( TCID ) in social media . In particular , we test the hypothesis that embeddings with context are more effective , by multi-tasking the learning of multilingual word embeddings and text classification ; we explore neural architectures for CLTC ; and we move from bi- to multi-lingual word embeddings . For all architectures , types of word embeddings and datasets , we notice a consistent gain trend in favor of multilingual joint training , especially for low-resourced languages .
We use the original ( monolingual ) model to generate sentence embeddings for the source language and then train a new system on translated sentences to mimic the original model . Compared to other methods for training multilingual sentence embeddings , this approach has several advantages : It is easy to extend existing models with relatively few samples to new languages , it is easier to ensure desired properties for the vector space , and the hardware requirements for training is lower . We demonstrate the effectiveness of our approach for 10 languages from various language families .
We argue that minor senses play an important role in word similarity computations , hence we use an unsupervised sense inventory resource to retrofit monolingual word embeddings , producing sense-aware embeddings . Using retrofitted sense-aware embeddings , we show improved word similarity and relatedness results on multiple word embeddings and multiple established word similarity tasks , sometimes up to an impressive margin of + 0.15 Spearman correlation score .
However , it has also been shown that for distant language pairs such fully unsupervised self-learning methods are unstable and often get stuck in poor local optima due to reduced isomorphism between starting monolingual spaces . In this work , we propose a new robust framework for learning unsupervised multilingual word embeddings that mitigates the instability issues . We learn a shared multilingual embedding space for a variable number of languages by incrementally adding new languages one by one to the current multilingual space .
document : Massively Multilingual Sentence Embeddings for Zero - Shot Cross - Lingual Transfer and Beyond We introduce an architecture to learn joint multilingual sentence representations for 93 languages , belonging to more than 30 different language families and written in 28 different scripts .
section : Related work Following the success of word embeddings mikolov2013distributed , pennington2014glove , there has been an increasing interest in learning continuous vector representations of longer linguistic units like sentences . These sentence embeddings are commonly obtained using a Recurrent Neural Network ( RNN ) encoder , which is typically trained in an unsupervised way over large collections of unlabelled corpora .
While the previous methods consider a single language at a time , multilingual representations have attracted a large attention in recent times . Most of this research focuses on cross - lingual word embeddings ruder2017survey , which are commonly learned jointly from parallel corpora gouws2015bilbowa , luong2015bilingual . An alternative approach that is becoming increasingly popular is to train word embeddings independently for each language over monolingual corpora , and then map them to a shared space based on a bilingual dictionary mikolov2013exploiting , artetxe2018generalizing or even in a fully unsupervised manner .
It can be seen that reshaping the embeddings is of no benefit , especially on WN18RR . These results indicate that the purpose of convolution on word embeddings is not to find patterns in a 2D embedding ( as with images ) , but perhaps to limit the number of dimensions that can interact with each other , thereby avoiding overfitting . Label Smoothing Contrary to the ablation study of dettmers2017convolutional , showing the influence of hyperparameters on mean reciprocal rank for FB15k - 237 , from which they deem label smoothing unimportant , we find label smoothing to give a significant improvement in prediction scores for WN18RR .
While the character component learns general regularities that are shared between all the words , individual word embeddings provide a way for the model to store word - specific information and any exceptions . Therefore , while we want the character - based model to shift towards predicting high - quality word embeddings , it is not desireable to optimise the word embeddings towards the character - level representations . This can be achieved by making sure that the optimisation is performed only in one direction ; in Theano , the disconnected_grad function gives the desired effect .
Kim2016 implemented a language model where encoding is performed by a convolutional network and LSTM over characters , whereas predictions are given on the word - level . Cao2016 proposed a method for learning both word embeddings and morphological segmentation with a bidirectional recurrent network over characters . There is also research on performing parsing and text classification with character - level neural models .
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 . In this paper , we propose a new method for this task based on multilingual sentence embeddings . Our approach uses an encoder - decoder trained over an initial parallel corpus to build multilingual sentence representations , which are then incorporated into a new margin - based method to score , mine and filter parallel sentences .
In comparison , our approach is conceptually simpler , and obtains substantially better results ( e.g. 48.9 vs 83.3 P@1 on English - French UN reconstruction ) . section : Multilingual sentence embeddings Following recent trend , we use a sequence - to - sequence encoder - decoder to learn our sentence embeddings Schwenk:2017:repl4nlp , espana2017empirical , hassan2018achieving , schwenk2018filtering .
CNN has been very successful on image classification ; see e.g. , the winning solutions of ImageNet Large Scale Visual Recognition Challenge . On text , since the work on token - level applications ( e.g. , POS tagging ) by nnnlpJMLR11 , CNN has been used in systems for entity search , sentence modeling , word embedding learning , product feature mining , and so on . Notably , in many of these CNN studies on text , the first layer of the network converts words in sentences to word vectors by table lookup .
We observe that the quality of learned word embedding distribution keeps improving as goes up from 0 to 0.1 , but starts to drop around . We also find that this optimal in word embedding learning ( i.e. , 0.1 ) is larger than the which yielded the best tagging performance on development sets ( i.e. , 0.01 or 0.05 ) . A possible explanation is that while word embeddings can adapt to relatively large ( e.g. , 0.1 ) during training , as adversarial perturbations are generated at the embedding level , such could change the semantics of the input from the current tagging model ’s perspective and hinder the training of tagging .
Moreover , the fact that the two components are separately trained can limit its expressiveness . levy2014neural also considered the matrix factorization perspective , but in the context of learning word embeddings . In a general sense , Mixture of Softmaxes proposed in this work can be seen as a particular instantiation of the long - existing idea called Mixture of Experts ( MoE ) jacobs1991adaptive .
Early approaches to deep semi - supervised learning pre - train neural models on unlabeled data , which has been successful for applications in computer vision jarrett2009best , lecun2010convolutional and NLP . Particularly noteworthy for NLP are algorithms for learning effective word embeddings and language model pretraining dai2015semi , ramachandran2016unsupervised , peters2018deep , howard2018universal , radford2018improving . Pre - training on other tasks such as machine translation has also been studied McCann2017LearnedIT .
Specifically , we first input one - hot - encoding character sequences to a convolution layer to enhance the semantic nonlinear representation ability of our model , and the output is then fed into a multi - gram ( i.e. different window sizes ) convolution layer to capture different local character chunk information . For word - level embedding , we use pre - trained word vectors , GloVe , to map each word to a low - dimensional vector space . Finally , each word is represented as a concatenation of the character - level embedding and word - level embedding .
The Applications of Stochastic Models in Network Embedding: A Survey Resource Analysis of Network Virtualization through User and Network ComNE: Reinforcing Network Embedding with Community Learning Efficient Algorithms for Constructing Multiplex Networks Embedding Graph Representation Learning via Hard and Channel-Wise Attention Networks Network representation learning: models, methods and applications Network Sampling Using k-hop Random Walks for Heterogeneous Network Embedding LINE: Large-scale Information Network Embedding Learning to Make Predictions on Graphs with Autoencoders Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Artificial Agents Learn Flexible Visual Representations by Playing a Hiding Game Domain Representation for Knowledge Graph Embedding Jiffy: A Convolutional Approach to Learning Time Series Similarity t-PINE: tensor-based predictable and interpretable node embeddings DeepWalk: online learning of social representations SVDNet for Pedestrian Retrieval Density estimation using Real NVP The Variational Fair Autoencoder Unifying Count-Based Exploration and Intrinsic Motivation Multi-Task Deep Neural Networks for Natural Language Understanding Design and Analysis of Hierarchical Ring Networks for Shared-Memory Multiprocessors A Graph Embedding Method for Wireless Sensor Networks Localization A collaborative representation based projections method for feature extraction A collaborative representation based projections method for feature extraction Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion Kuratowski-type theorems do not extend to pseudosurfaces Interval Hypergraphs Neural Natural Language Inference Models Enhanced with External Knowledge Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks ReasoNet: Learning to Stop Reading in Machine Comprehension Revisiting Semi-Supervised Learning with Graph Embeddings LINE: Large-scale Information Network Embedding A Word Embeddings Training Method Based on Modified Skip-Gram and Align Building Earth Mover's Distance on Bilingual Word Embeddings for Machine Translation Cross-Lingual Approaches to Reference Resolution in Dialogue Systems Learning Multilingual Word Embeddings Using Image-Text Data Large-Scale Regularized Sumcor GCCA via Penalty-Dual Decomposition Word Embeddings for Code-Mixed Language Processing Distributional semantics and machine learning for statistical machine translation NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings Expanding the Text Classification Toolbox with Cross-Lingual Embeddings Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond Hypernetwork Knowledge Graph Embeddings Attending to Characters in Neural Sequence Labeling Models Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Robust Multilingual Part-of-Speech Tagging via Adversarial Training Breaking the Softmax Bottleneck: A High-Rank RNN Language Model Semi-Supervised Sequence Modeling with Cross-View Training A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
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WI ICONIP (4) WWW '15 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) ArXiv KDD '14 2017 IEEE International Conference on Computer Vision (ICCV) ICLR ICLR NIPS ACL ICPP (1) GLOBECOM AAAI ACL ArXiv CoCo@NIPS ICML WWW '15 ICCCS (6) AAAI ICASSP EMNLP EMNLP LREC NAACL-HLT (1) Transactions of the Association for Computational Linguistics ICANN COLING ACL HLT-NAACL NAACL-HLT ICLR EMNLP ACL
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