In light of the success of deep learning in computer vision , deep-learning-based video prediction emerged as a promising research direction . Defined as a self-supervised learning task , video prediction represents a suitable framework for representation learning , as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos . Motivated by the increasing interest in this task , we provide a review on the deep learning methods for prediction in video sequences . We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data . Our framework casts agent modeling as a representation learning problem . Consequently , we construct a novel objective inspired by imitation learning and agent identification and design an algorithm for unsupervised learning of representations of agent policies . Such relationship is not limited to object of the target category , but also includes surrounding objects of other categories . We show that image regions with multiple contexts and multiple rotations are effective in capturing such relationship during the representation learning process and in handling the semantic and visual variation caused by different window-object configurations . Experimental results show that the representation learned by our approach can improve the object detection accuracy by 6.4 % in mean average precision ( mAP ) on ILSVRC2014 . Many existing methods , such as diagnostic Cost Groups ( DCG ) , rely on expert knowledge to build patient representation from medical data , which is resource consuming and non-scalable . Unsupervised machine learning algorithms are a good choice for automating the representation learning process . However , there is very little research focusing on onpatient-level representation learning directly from medical claims . In this study , the possibility of using a deep learning method , which is an unsupervised representation learning method to extract features from airborne raw full-waveform data , is investigated . Thus , a novel end-to-end autoencoder network called FWNetAE is proposed to address representation learning challenges on raw full-waveform data . On the encoder side , a PointNet-based network extracts the latent vector of raw full-waveform data . Thus , robust representation learning by removing noisy features or samples , complementing incomplete data , and mitigating the distribution difference becomes the key . Along this line of research , low-rank modeling has been widely-applied to solving representation learning challenges . This survey covers the topic from a knowledge flow perspective in terms of : ( 1 ) robust knowledge recovery , ( 2 ) robust knowledge transfer , and ( 3 ) robust knowledge fusion , centered around several major applications . This judicial combination of representation and classification shows improved recognition accuracy on benchmark databases . The performance of the proposed approach compared to another state-of-the-art technique on other representation based learning is established with extensive experimental . Advantage of the proposed approach is also shown by the performance analysis with single training image , which is necessary for some real time applications . Specifically , in the signal neighborhood selection phase , GDPNet optimizes the neighborhood for each target node by formulating the process of removing noisy neighborhoods as a Markov decision process and learning a policy with task-specific rewards received from the representation learning phase . In the representation learning phase , GDPNet aggregates features from signal neighbors to generate node representations for down-stream tasks , and provides task-specific rewards to the signal neighbor selection phase . These two phases are jointly trained to select optimal sets of neighbors for target nodes with maximum cumulative task-specific rewards , and to learn robust representations for nodes . Unlike previous approaches that mainly focus on one specific kind of misalignment , our proposed method can eliminate the feature bias that is introduced by multiple misalignment problems . Systematic visualizations and comparisons are conducted to demonstrate that our method can efficiently correct the feature bias during the representation learning step . Evaluations on three large-scale datasets show that our proposed bias-free representation learning method can outperform the state-of-the-art methods . This problem has been observed in several previous works bowman , vlae and different techniques such as annealing the weight of the KL term bowman or weakening the decoder vlae have been proposed to make and more dependent . As suggested in ference_ml , vlae , we think that the maximum likelihood objective by itself is not a useful objective for representation learning especially when a powerful decoder is used . In PixelGAN autoencoders , in order to encourage learning more useful representations , we modify the ELBO ( elbo ] Equation [ reference ] ) by removing the mutual information term from it , since this term is explicitly encouraging to become independent of . Generative adversarial networks ( GANs ) aim to model the natural image distribution by forcing the generated samples to be indistinguishable from natural images . GANs enable a wide variety of applications such as image generation , representation learning , image manipulation , object detection , and video applications . Various coarse - to - fine schemes have been proposed to synthesize larger images ( e.g. ) in an unconditional setting . The first question we asked was whether the auxiliary task was necessary in our algorithm . We evaluated this by dropping the reconstruction module from the network to test whether the initial random noise generated by the SR is enough to drive representation learning . It is not . Given a large graph , learn graph representations that can be used to infer unseen graph properties such as node types and missing edges . We propose a framework for learning representations for classes of directed and undirected graphs . The graphs may have nodes and edges with multiple discrete and continuous attributes and may have multiple types of edges . In Sections 2 and 3 , we discuss the problem formulation of classification in data networks , and how it relates to our work . In Section 4 we present DeepWalk , our approach for Social Representation Learning . We outline ours experiments in Section 5 , and present their results in Section 6 . In terms of vertex representation modeling , this yields the optimization problem : We find these relaxations are particularly desirable for social representation learning . First , the order independence assumption better captures a sense of ' nearness ' that is provided by random walks . A number of techniques for generating features from graphs have also been proposed [ reference ][ reference ][ reference ][ reference ][ reference ] . In contrast to these methods , we frame the feature creation procedure as a representation learning problem . Graph Kernels [ reference ] have been proposed as a way to use relational data as part of the classification process , but are quite slow unless approximated [ reference ] . Our approach is complementary ; instead of encoding the structure as part of a kernel function , we learn a representation which allows them to be used directly as features for any classification method . In networks , the conventional paradigm for generating features for nodes is based on feature extraction techniques which typically involve some seed hand - crafted features based on network properties . In contrast , our goal is to automate the whole process by casting feature extraction as a representation learning problem in which case we do not require any hand - engineered features . Unsupervised feature learning approaches typically exploit the spectral properties of various matrix representations of graphs , especially the Laplacian and the adjacency matrices . We have demonstrated that our proposed xDeepFM has two special properties in comparison with these models : ( 1 ) xDeepFM learns high - order feature interactions in both explicit and implicit fashions ; ( 2 ) xDeepFM learns feature interactions at the vector - wise level rather than at the bit - wise level . subsubsection : Deep Learning for Elaborate Representation Learning We include some other deep learning - based RSs in this section due to that they are less focused on learning feature interactions . Among these , methods such as C - PACE and metric - require a good metric on the state space that satisfies the assumptions of the algorithm . The corresponding representation learning issue has some parallels to the representation problem that we address by using an autoecoder , but it is unclear how the appropriate metric for the prior methods can be acquired automatically on tasks with raw sensory input , such as the Atari games in our experimental evaluation . Methods based on Monte - Carlo tree search can also scale gracefully to complex domains , and indeed previous work has applied such techniques to the task of playing Atari games from screen images . We will use this data to investigate the nature of relationship between amount of data and performance on vision tasks . Specifically , we will look into the power of data for visual representation learning ( pre - training ) . We evaluate our learned representation on a variety of vision tasks : image classification , object detection , semantic segmentation and human pose estimation . Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications . The recent great improvements on benchmark data sets , which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters , usually require many tedious tricks and much expertise to tune . Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition . Optimal component analysis ( OCA ) formulates the problem in the framework of optimization on a Grassmann manifold and a stochastic gradient method is used to estimate the optimal basis . Learning rich representations efficiently plays an important role in multi-modal recognition task , which is crucial to achieve high generalization performance . To address this problem , in this paper , we propose an effective Multi-Modal Deep Extreme Learning Machine ( MM-DELM ) structure , while maintaining ELM ’s advantages of training efficiency . In MR-iNE , we learn the structural representation for relationships of entities by preserving first-order , second-order , and high-order proximity influence structure and mapping them to a low-dimensional space , where fuse information from the aligned semi-supervised component that maximize the likelihood of preserving both the global and local structure of the influence relations of vertexes to overcome data sparsity . Our experiment results show the effectiveness of MR-iNE outperforms the state-of-the-art algorithms for learning rich representations on multi-label classification in real datasets from diverse domains . Many of our everyday tasks require the control of the serial order and the timing of component actions . Using the dynamic neural field ( DNF ) framework , we address the learning of representations that support the performance of precisely time action sequences . In continuation of previous modeling work and robotics implementations , we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation . Learning of representations usually happens in different ways . Sometimes it persuades sparsity thus enhances performance through the task categorization . Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning . In this paper , we tackle the problem of learning representations invariant to a specific factor or trait of data . Multimodal models have been proven to outperform text-based approaches on learning semantic representations . However , it still remains unclear what properties are encoded in multimodal representations , in what aspects do they outperform the single-modality representations , and what happened in the process of semantic compositionality in different input modalities . Learning semantic representations and tree structures of bilingual phrases is beneficial for statistical machine translation . In this paper , we propose a new neural network model called Bilingual Correspondence Recursive Autoencoder ( BCorrRAE ) to model bilingual phrases in translation . In many image-related tasks , learning expressive and discriminative representations of images is essential , and deep learning has been studied for automating the learning of such representations . Some user-centric tasks , such as image recommendations , call for effective representations of not only images but also preferences and intents of users over images . Therefore , to determine the similarity between the document and the profile , we used a learning model that exploits the users ’ explicit pertinence judgments . In this context , we have proposed learning semantic features related to the user 's profile ( represented by hierarchies of concepts ) . The predicted model will then be used in the ordering phase to classify other documents that result from a new query submitted by the user . In successful applications of LTAL , the examples selected by the oracle policy do not substantially depend on the optimization procedure , while in our setup the stochastic nature of optimization strongly affects the examples selected by the oracle . We conclude that the current applicability of LTAL for improving data efficiency in learning semantic meaning representations is limited . Moreover , they can not fully explore the mutual interactions among the semantic regions and do not explicitly model the label co-occurrence . To address these issues , we propose a Semantic-Specific Graph Representation Learning ( SSGRL ) framework that consists of two crucial modules : 1 ) a semantic decoupling module that incorporates category semantics to guide learning semantic-specific representations and 2 ) a semantic interaction module that correlates these representations with a graph built on the statistical label co-occurrence and explores their interactions via a graph propagation mechanism . Extensive experiments on public benchmarks show that our SSGRL framework outperforms current state-of-the-art methods by a sizable margin , e.g. with an mAP improvement of 2.5 % , 2.6 % , 6.7 % , and 3.1 % on the PASCAL VOC 2007 & 2012 , Microsoft-COCO and Visual Genome benchmarks , respectively . RNN can capitalize on distributed representations of words by first converting the tokens comprising each text into vectors , which form a matrix . This matrix includes two dimensions : the time - step dimension and the feature vector dimension , and it will be updated in the process of learning feature representation . Then RNN utilizes 1D max pooling operation or attention - based operation , which extracts maximum values or generates a weighted representation over the time - step dimension of the matrix , to obtain a fixed - length vector . , Pennington2014GloveGV are a key component in many neural language understanding models . However , learning high quality representations can be challenging . They should ideally model both ( 1 ) complex characteristics of word use ( e.g. , syntax and semantics ) , and ( 2 ) how these uses vary across linguistic contexts ( i.e. , to model polysemy ) . How important is representational capacity ? Is the number of classes a key factor in learning visual representation ? How could clean data ( , ImageNet ) help improve the visual representations ? Graph representation learning is a fundamental task of various applications , aiming to learn low-dimensional embeddings for nodes which can preserve graph topology information . However , many existing methods focus on static graphs while ignoring graph evolving patterns . Graphs are complex objects that do not lend themselves easily to typical learning tasks . Recently , a range of approaches based on graph kernels or graph neural networks have been developed for graph classification and for representation learning on graphs in general . As the developed methodologies become more sophisticated , it is important to understand which components of the increasingly complex methods are necessary or most effective . Graph Convolutional Networks ( GCNs ) have been widely studied for graph data representation and learning . The graph convolution operation in GCNs can usually be regarded as a composition of feature aggregation/propagation and transformation . Graph Convolutional Networks ( GCNs ) have shown very powerful for graph data representation and learning tasks . Existing GCNs usually conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors which is biased by its own representation . An essential problem arose is how to effectively and efficiently learn the representation for objects in graphs . In this thesis , both the effectiveness as well as efficiency aspects of the graph representation learning problem are addressed . Specifically , we start by proposing an effective approach for learning heterogeneous graph embedding in an unsupervised setting . Thereby , a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space , such that the graph properties can be preserved . Although tremendous efforts have been made to address the graph representation learning problem , many of them still suffer from their shallow learning mechanisms . Deep learning models on graphs ( e.g. , graph neural networks ) have recently emerged in machine learning and other related areas , and demonstrated the superior performance in various problems . 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 . Parametric graph embedding via siamese architecture . Abstract Graph representation learning or graph embedding is a classical topic in data mining . Current embedding methods are mostly non-parametric , where all the embedding points are unconstrained free points in the target space . Authors have often raised concerns about their lack in scientific publications to improve the quality of the field . Recently , the graph representation learning field has attracted the attention of a wide research community , which resulted in a large stream of works . As such , several Graph Neural Network models have been developed to effectively tackle graph classification . Pykg2vec 's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph embedding algorithms , and is designed to easily incorporate new algorithms . The goal of pykg2vec is to provide a practical and educational platform to accelerate research in knowledge graph representation learning . Pykg2vec is built on top of TensorFlow and Python 's multiprocessing framework and provides modules for batch generation , Bayesian hyperparameter optimization , mean rank evaluation , embedding , and result visualization . Second , a local autoencoder edge representation learning method is developed to design K local representation learning models , each with individual parameters and structure to perform local representation learning on each of K classification-based subgraphs with unique local characteristics and jointly optimize the loss functions within and across classes . Third but last , we propose a density-adaptive edge representation learning method with the optimization at both edge and subgraph levels to address the representation learning of graph data with highly imbalanced vertex degree and edge distribution . section : Related Work Our model draws inspiration from the research fields of graph representation learning , neural networks on graphs , and neural encoder - decoder models . Graph Representation Learning . ‡ Santa Barbara Cottage Hospital { carlostorres , archith , manju}@ece.ucsb.edu , jfried@sbch.org . 2016 . RAM : Role Representation and Identification from combined Appearance and Activity Maps . In Proceedings of ICDSC , 2017 , Session on Target Tracking and Person Re-Identification , 7 pages . Information literacy instruction , research and other scholarly activities , distance and online learning as well as embedding in classrooms , were described as ways of ensuring successful embedding librarianship . Implications The roles reported in the literature should inform practicing librarians contemplating embedding practices , guide formal embedded librarianship programs , and encourage other librarians to consider new skills in support of embedding roles . Originality/value The paper is the first to develop systematic literature review on embedding librarianship roles and offers the reference list of those selected papers and identifying further papers . In this paper , we proposed a Semantic Tensor Product ( STP ) model aimed at finding a more grammatically correct caption for a given image . We tried two kinds of ways to integrate semantic concepts ( i.e. tags ) into Tensor Product Representation ( TPR ) in the process of image caption generation , where Tensor Product Representation has the advantage of decoding a role embedding using a corresponding unbinding vector in any position directly , while semantic concepts could provide crucial clues for internal TPR prediction . We qualitatively analyze the two proposed STP based models , and quantitatively evaluate them on benchmark dataset : MSCOCO . DCI ( Data , Context and Interaction ) comes from role-based programming and separates the system state from its behavior making it possible to preserve use cases in code to the great extent . In its Java implementation , DCI relies on the Qi4J ( renamed to Apache Zest at the time of finalizing this paper ) framework for role injection . This paper provides an assessment of DCI via its Qi4J implementation and beyond based on an independent study of a small car dealer system development .
In light of the success of deep learning in computer vision , deep-learning-based video prediction emerged as a promising research direction . Defined as a self-supervised learning task , video prediction represents a suitable framework for representation learning , as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos . Motivated by the increasing interest in this task , we provide a review on the deep learning methods for prediction in video sequences .
We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data . Our framework casts agent modeling as a representation learning problem . Consequently , we construct a novel objective inspired by imitation learning and agent identification and design an algorithm for unsupervised learning of representations of agent policies .
Such relationship is not limited to object of the target category , but also includes surrounding objects of other categories . We show that image regions with multiple contexts and multiple rotations are effective in capturing such relationship during the representation learning process and in handling the semantic and visual variation caused by different window-object configurations . Experimental results show that the representation learned by our approach can improve the object detection accuracy by 6.4 % in mean average precision ( mAP ) on ILSVRC2014 .
Many existing methods , such as diagnostic Cost Groups ( DCG ) , rely on expert knowledge to build patient representation from medical data , which is resource consuming and non-scalable . Unsupervised machine learning algorithms are a good choice for automating the representation learning process . However , there is very little research focusing on onpatient-level representation learning directly from medical claims .
In this study , the possibility of using a deep learning method , which is an unsupervised representation learning method to extract features from airborne raw full-waveform data , is investigated . Thus , a novel end-to-end autoencoder network called FWNetAE is proposed to address representation learning challenges on raw full-waveform data . On the encoder side , a PointNet-based network extracts the latent vector of raw full-waveform data .
Thus , robust representation learning by removing noisy features or samples , complementing incomplete data , and mitigating the distribution difference becomes the key . Along this line of research , low-rank modeling has been widely-applied to solving representation learning challenges . This survey covers the topic from a knowledge flow perspective in terms of : ( 1 ) robust knowledge recovery , ( 2 ) robust knowledge transfer , and ( 3 ) robust knowledge fusion , centered around several major applications .
This judicial combination of representation and classification shows improved recognition accuracy on benchmark databases . The performance of the proposed approach compared to another state-of-the-art technique on other representation based learning is established with extensive experimental . Advantage of the proposed approach is also shown by the performance analysis with single training image , which is necessary for some real time applications .
Specifically , in the signal neighborhood selection phase , GDPNet optimizes the neighborhood for each target node by formulating the process of removing noisy neighborhoods as a Markov decision process and learning a policy with task-specific rewards received from the representation learning phase . In the representation learning phase , GDPNet aggregates features from signal neighbors to generate node representations for down-stream tasks , and provides task-specific rewards to the signal neighbor selection phase . These two phases are jointly trained to select optimal sets of neighbors for target nodes with maximum cumulative task-specific rewards , and to learn robust representations for nodes .
Unlike previous approaches that mainly focus on one specific kind of misalignment , our proposed method can eliminate the feature bias that is introduced by multiple misalignment problems . Systematic visualizations and comparisons are conducted to demonstrate that our method can efficiently correct the feature bias during the representation learning step . Evaluations on three large-scale datasets show that our proposed bias-free representation learning method can outperform the state-of-the-art methods .
This problem has been observed in several previous works bowman , vlae and different techniques such as annealing the weight of the KL term bowman or weakening the decoder vlae have been proposed to make and more dependent . As suggested in ference_ml , vlae , we think that the maximum likelihood objective by itself is not a useful objective for representation learning especially when a powerful decoder is used . In PixelGAN autoencoders , in order to encourage learning more useful representations , we modify the ELBO ( elbo ] Equation [ reference ] ) by removing the mutual information term from it , since this term is explicitly encouraging to become independent of .
Generative adversarial networks ( GANs ) aim to model the natural image distribution by forcing the generated samples to be indistinguishable from natural images . GANs enable a wide variety of applications such as image generation , representation learning , image manipulation , object detection , and video applications . Various coarse - to - fine schemes have been proposed to synthesize larger images ( e.g. ) in an unconditional setting .
The first question we asked was whether the auxiliary task was necessary in our algorithm . We evaluated this by dropping the reconstruction module from the network to test whether the initial random noise generated by the SR is enough to drive representation learning . It is not .
Given a large graph , learn graph representations that can be used to infer unseen graph properties such as node types and missing edges . We propose a framework for learning representations for classes of directed and undirected graphs . The graphs may have nodes and edges with multiple discrete and continuous attributes and may have multiple types of edges .
In Sections 2 and 3 , we discuss the problem formulation of classification in data networks , and how it relates to our work . In Section 4 we present DeepWalk , our approach for Social Representation Learning . We outline ours experiments in Section 5 , and present their results in Section 6 .
In terms of vertex representation modeling , this yields the optimization problem : We find these relaxations are particularly desirable for social representation learning . First , the order independence assumption better captures a sense of ' nearness ' that is provided by random walks .
A number of techniques for generating features from graphs have also been proposed [ reference ][ reference ][ reference ][ reference ][ reference ] . In contrast to these methods , we frame the feature creation procedure as a representation learning problem . Graph Kernels [ reference ] have been proposed as a way to use relational data as part of the classification process , but are quite slow unless approximated [ reference ] . Our approach is complementary ; instead of encoding the structure as part of a kernel function , we learn a representation which allows them to be used directly as features for any classification method .
In networks , the conventional paradigm for generating features for nodes is based on feature extraction techniques which typically involve some seed hand - crafted features based on network properties . In contrast , our goal is to automate the whole process by casting feature extraction as a representation learning problem in which case we do not require any hand - engineered features . Unsupervised feature learning approaches typically exploit the spectral properties of various matrix representations of graphs , especially the Laplacian and the adjacency matrices .
We have demonstrated that our proposed xDeepFM has two special properties in comparison with these models : ( 1 ) xDeepFM learns high - order feature interactions in both explicit and implicit fashions ; ( 2 ) xDeepFM learns feature interactions at the vector - wise level rather than at the bit - wise level . subsubsection : Deep Learning for Elaborate Representation Learning We include some other deep learning - based RSs in this section due to that they are less focused on learning feature interactions .
Among these , methods such as C - PACE and metric - require a good metric on the state space that satisfies the assumptions of the algorithm . The corresponding representation learning issue has some parallels to the representation problem that we address by using an autoecoder , but it is unclear how the appropriate metric for the prior methods can be acquired automatically on tasks with raw sensory input , such as the Atari games in our experimental evaluation . Methods based on Monte - Carlo tree search can also scale gracefully to complex domains , and indeed previous work has applied such techniques to the task of playing Atari games from screen images .
We will use this data to investigate the nature of relationship between amount of data and performance on vision tasks . Specifically , we will look into the power of data for visual representation learning ( pre - training ) . We evaluate our learned representation on a variety of vision tasks : image classification , object detection , semantic segmentation and human pose estimation .
Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications . The recent great improvements on benchmark data sets , which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters , usually require many tedious tricks and much expertise to tune .
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition . Optimal component analysis ( OCA ) formulates the problem in the framework of optimization on a Grassmann manifold and a stochastic gradient method is used to estimate the optimal basis .
Learning rich representations efficiently plays an important role in multi-modal recognition task , which is crucial to achieve high generalization performance . To address this problem , in this paper , we propose an effective Multi-Modal Deep Extreme Learning Machine ( MM-DELM ) structure , while maintaining ELM ’s advantages of training efficiency .
In MR-iNE , we learn the structural representation for relationships of entities by preserving first-order , second-order , and high-order proximity influence structure and mapping them to a low-dimensional space , where fuse information from the aligned semi-supervised component that maximize the likelihood of preserving both the global and local structure of the influence relations of vertexes to overcome data sparsity . Our experiment results show the effectiveness of MR-iNE outperforms the state-of-the-art algorithms for learning rich representations on multi-label classification in real datasets from diverse domains .
Many of our everyday tasks require the control of the serial order and the timing of component actions . Using the dynamic neural field ( DNF ) framework , we address the learning of representations that support the performance of precisely time action sequences . In continuation of previous modeling work and robotics implementations , we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation .
Learning of representations usually happens in different ways . Sometimes it persuades sparsity thus enhances performance through the task categorization .
Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning . In this paper , we tackle the problem of learning representations invariant to a specific factor or trait of data .
Multimodal models have been proven to outperform text-based approaches on learning semantic representations . However , it still remains unclear what properties are encoded in multimodal representations , in what aspects do they outperform the single-modality representations , and what happened in the process of semantic compositionality in different input modalities .
Learning semantic representations and tree structures of bilingual phrases is beneficial for statistical machine translation . In this paper , we propose a new neural network model called Bilingual Correspondence Recursive Autoencoder ( BCorrRAE ) to model bilingual phrases in translation .
In many image-related tasks , learning expressive and discriminative representations of images is essential , and deep learning has been studied for automating the learning of such representations . Some user-centric tasks , such as image recommendations , call for effective representations of not only images but also preferences and intents of users over images .
Therefore , to determine the similarity between the document and the profile , we used a learning model that exploits the users ’ explicit pertinence judgments . In this context , we have proposed learning semantic features related to the user 's profile ( represented by hierarchies of concepts ) . The predicted model will then be used in the ordering phase to classify other documents that result from a new query submitted by the user .
In successful applications of LTAL , the examples selected by the oracle policy do not substantially depend on the optimization procedure , while in our setup the stochastic nature of optimization strongly affects the examples selected by the oracle . We conclude that the current applicability of LTAL for improving data efficiency in learning semantic meaning representations is limited .
Moreover , they can not fully explore the mutual interactions among the semantic regions and do not explicitly model the label co-occurrence . To address these issues , we propose a Semantic-Specific Graph Representation Learning ( SSGRL ) framework that consists of two crucial modules : 1 ) a semantic decoupling module that incorporates category semantics to guide learning semantic-specific representations and 2 ) a semantic interaction module that correlates these representations with a graph built on the statistical label co-occurrence and explores their interactions via a graph propagation mechanism . Extensive experiments on public benchmarks show that our SSGRL framework outperforms current state-of-the-art methods by a sizable margin , e.g. with an mAP improvement of 2.5 % , 2.6 % , 6.7 % , and 3.1 % on the PASCAL VOC 2007 & 2012 , Microsoft-COCO and Visual Genome benchmarks , respectively .
RNN can capitalize on distributed representations of words by first converting the tokens comprising each text into vectors , which form a matrix . This matrix includes two dimensions : the time - step dimension and the feature vector dimension , and it will be updated in the process of learning feature representation . Then RNN utilizes 1D max pooling operation or attention - based operation , which extracts maximum values or generates a weighted representation over the time - step dimension of the matrix , to obtain a fixed - length vector .
, Pennington2014GloveGV are a key component in many neural language understanding models . However , learning high quality representations can be challenging . They should ideally model both ( 1 ) complex characteristics of word use ( e.g. , syntax and semantics ) , and ( 2 ) how these uses vary across linguistic contexts ( i.e. , to model polysemy ) .
How important is representational capacity ? Is the number of classes a key factor in learning visual representation ? How could clean data ( , ImageNet ) help improve the visual representations ?
Graph representation learning is a fundamental task of various applications , aiming to learn low-dimensional embeddings for nodes which can preserve graph topology information . However , many existing methods focus on static graphs while ignoring graph evolving patterns .
Graphs are complex objects that do not lend themselves easily to typical learning tasks . Recently , a range of approaches based on graph kernels or graph neural networks have been developed for graph classification and for representation learning on graphs in general . As the developed methodologies become more sophisticated , it is important to understand which components of the increasingly complex methods are necessary or most effective .
Graph Convolutional Networks ( GCNs ) have been widely studied for graph data representation and learning . The graph convolution operation in GCNs can usually be regarded as a composition of feature aggregation/propagation and transformation .
Graph Convolutional Networks ( GCNs ) have shown very powerful for graph data representation and learning tasks . Existing GCNs usually conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors which is biased by its own representation .
An essential problem arose is how to effectively and efficiently learn the representation for objects in graphs . In this thesis , both the effectiveness as well as efficiency aspects of the graph representation learning problem are addressed . Specifically , we start by proposing an effective approach for learning heterogeneous graph embedding in an unsupervised setting .
Thereby , a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space , such that the graph properties can be preserved . Although tremendous efforts have been made to address the graph representation learning problem , many of them still suffer from their shallow learning mechanisms . Deep learning models on graphs ( e.g. , graph neural networks ) have recently emerged in machine learning and other related areas , and demonstrated the superior performance in various problems .
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 .
Parametric graph embedding via siamese architecture . Abstract Graph representation learning or graph embedding is a classical topic in data mining . Current embedding methods are mostly non-parametric , where all the embedding points are unconstrained free points in the target space .
Authors have often raised concerns about their lack in scientific publications to improve the quality of the field . Recently , the graph representation learning field has attracted the attention of a wide research community , which resulted in a large stream of works . As such , several Graph Neural Network models have been developed to effectively tackle graph classification .
Pykg2vec 's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph embedding algorithms , and is designed to easily incorporate new algorithms . The goal of pykg2vec is to provide a practical and educational platform to accelerate research in knowledge graph representation learning . Pykg2vec is built on top of TensorFlow and Python 's multiprocessing framework and provides modules for batch generation , Bayesian hyperparameter optimization , mean rank evaluation , embedding , and result visualization .
Second , a local autoencoder edge representation learning method is developed to design K local representation learning models , each with individual parameters and structure to perform local representation learning on each of K classification-based subgraphs with unique local characteristics and jointly optimize the loss functions within and across classes . Third but last , we propose a density-adaptive edge representation learning method with the optimization at both edge and subgraph levels to address the representation learning of graph data with highly imbalanced vertex degree and edge distribution .
section : Related Work Our model draws inspiration from the research fields of graph representation learning , neural networks on graphs , and neural encoder - decoder models . Graph Representation Learning .
‡ Santa Barbara Cottage Hospital { carlostorres , archith , manju}@ece.ucsb.edu , jfried@sbch.org . 2016 . RAM : Role Representation and Identification from combined Appearance and Activity Maps . In Proceedings of ICDSC , 2017 , Session on Target Tracking and Person Re-Identification , 7 pages .
Information literacy instruction , research and other scholarly activities , distance and online learning as well as embedding in classrooms , were described as ways of ensuring successful embedding librarianship . Implications The roles reported in the literature should inform practicing librarians contemplating embedding practices , guide formal embedded librarianship programs , and encourage other librarians to consider new skills in support of embedding roles . Originality/value The paper is the first to develop systematic literature review on embedding librarianship roles and offers the reference list of those selected papers and identifying further papers .
In this paper , we proposed a Semantic Tensor Product ( STP ) model aimed at finding a more grammatically correct caption for a given image . We tried two kinds of ways to integrate semantic concepts ( i.e. tags ) into Tensor Product Representation ( TPR ) in the process of image caption generation , where Tensor Product Representation has the advantage of decoding a role embedding using a corresponding unbinding vector in any position directly , while semantic concepts could provide crucial clues for internal TPR prediction . We qualitatively analyze the two proposed STP based models , and quantitatively evaluate them on benchmark dataset : MSCOCO .
DCI ( Data , Context and Interaction ) comes from role-based programming and separates the system state from its behavior making it possible to preserve use cases in code to the great extent . In its Java implementation , DCI relies on the Qi4J ( renamed to Apache Zest at the time of finalizing this paper ) framework for role injection . This paper provides an assessment of DCI via its Qi4J implementation and beyond based on an independent study of a small car dealer system development .
A Review on Deep Learning Techniques for Video Prediction Learning Policy Representations in Multiagent Systems Window-Object Relationship Guided Representation Learning for Generic Object Detections Distributed representation of patients and its use for medical cost prediction FWNetAE: Spatial Representation Learning for Full Waveform Data Using Deep Learning Learning Robust Data Representation: A Knowledge Flow Perspective. A Class Specific Representation Learning for Illumination Tolerant Face Recognition Learning Robust Representations with Graph Denoising Policy Network Learning Bias-Free Representation for Large-Scale Person Re-Identification PixelGAN Autoencoders High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Learning Convolutional Neural Networks for Graphs DeepWalk: online learning of social representations node2vec: Scalable Feature Learning for Networks xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Hierarchical Extreme Learning Machine for unsupervised representation learning Learning representations for object classification using multi-stage optimal component analysis Multi-modal Deep Extreme Learning Machine for Robotic Grasping Recognition Structural Representations Learning of Social Influence in Heterogeneous Social Networks Learning joint representations for order and timing of perceptual-motor sequences: A dynamic neural field approach The appropriateness of k-Sparse autoencoders in sparse coding Controllable Invariance through Adversarial Feature Learning Investigating Inner Properties of Multimodal Representation and Semantic Compositionality with Brain-based Componential Semantics Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation Comparative Deep Learning of Hybrid Representations for Image Recommendations Learning-to-Rank for Hybrid User Profiles On the Limits of Learning to Actively Learn Semantic Representations Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling Deep contextualized word representations Revisiting Unreasonable Effectiveness of Data in Deep Learning Era K-Core based Temporal Graph Convolutional Network for Dynamic Graphs A simple yet effective baseline for non-attribute graph classification Graph Mask Convolutional Network GmCN: Graph Mask Convolutional Network Effective and Efficient Representation Learning for Graph Structures Graph convolutional networks: a comprehensive review Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity GraPASA: Parametric graph embedding via siamese architecture A Fair Comparison of Graph Neural Networks for Graph Classification Pykg2vec: A Python Library for Knowledge Graph Embedding Density-Adaptive Local Edge Representation Learning with Generative Adversarial Network Multi-label Edge Classification Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Role Representation and Re-identification from Appearance and Activity Maps Systematic Literature Review Informing LIS Professionals on Embedding Librarianship Roles Semantic Tensor Product for Image Captioning Assessing the DCI approach to preserving use cases in Code: Qi4J and beyond
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ISM RTIP2R (2) NIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICML KDD '14 KDD KDD ArXiv 2017 IEEE International Conference on Computer Vision (ICCV) IJCNN ITQM IJCNN EMNLP CVPR COLING NAACL-HLT 2017 IEEE International Conference on Computer Vision (ICCV) ICLR ICDM ArXiv BigCom
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