This paper summarizes ongoing research on a framework for representation learning using harmonic analysis , a subfield of mathematics . Harmonic analysis includes Fourier analysis , where new eigenvector representations are constructed by diagonalization of operators , and wavelet analysis , where new representations are constructed by dilation . In contrast to autoencoder , which reconstructs the input data itself , neighbor-encoder reconstructs the input data 's neighbors . As the proposed representation learning problem is essentially a neighbor reconstruction problem , domain knowledge can be easily incorporated in the form of an appropriate definition of similarity between objects . Based on that observation , our framework can leverage any off-the-shelf similarity search algorithms or side information to find the neighbor of an input object . The paper presents an original approach to music emotion recognition . We propose to use recurrent neural networks to separate the representation learning process from the classifier , which allows us to use a Support Vector Machine on top of a network to improve the results . We define a suitable loss function that is able to find a feature space in which similarity between vectors representing the music recordings corresponds to the similarity between their annotations . This can be accomplished using dimensionality reduction , clustering , or graph - based techniques . In our work , we draw on recent developments in representation learning with neural networks , as discussed in the following section . However , even with a good learned state representation , maintaining a table of visitation frequencies becomes impractical for complex tasks . ‘ Network engineering ’ ’ is increasingly more important for visual recognition research . In this paper , we aim to develop new path topology of deep architectures to further push the frontier of representation learning . In particular , we focus on analyzing and reforming the skip connection , which has been widely used in designing modern deep neural networks and offers remarkable success in many applications . kumar2015ask employ a structured neural network with episodic memory modules for natural language and also visual question answering . Similar to the above work , we leverage memory and attention in a recurrent neural network for inducing relations between tokens as a module in a larger network responsible for representation learning . As a property of soft attention , all intermediate relations we aim to capture are soft and differentiable . In contrast to traditional handdesigned features ( e.g. , SIFT [ reference ] and HOG [ reference ] ) , features learned by neural networks from large - scale data [ reference ] require minimal human involvement during training , and can be transferred to a variety of recognition tasks [ reference ][ reference ][ reference ] . Nevertheless , human effort has been shifted to designing better network architectures for learning representations . Designing architectures becomes increasingly difficult with the growing number of hyper - parameters ( width [ reference ] , filter sizes , strides , etc . ) , especially when there are many layers . Recent efforts sidestep this limitation through retrieving users ’ locations by alternatively unifying user generated contents ( e.g. , texts and public profiles ) and online social relations . Though achieving some progress , previous methods rely on the similarity of texts and/or neighboring nodes for user geolocation , which suffers the problems of : ( 1 ) location-agnostic problem of network representation learning , which largely impedes the performance of their prediction accuracy ; and ( 2 ) lack of interpretability w.r.t . the predicted results that is crucial for understanding model behavior and further improving prediction performance . However , there exists a lack of algorithm of using the future evolution results of the networks to guide the network representation learning . Therefore , this paper aims at modeling the future network evolution results of the networks based on the link prediction algorithm , introducing the future link probabilities between vertices without edges into the network representation learning tasks . In order to make the network representation vectors contain more feature factors , the text features of the vertices are also embedded into the network representation vectors . This is achieved by collecting multiple measurement points for each network and displaying them on a map . Our approach produces more accurate and detailed network representations on a map compared to existing applications on the market . Possible extensions to the current application include various analyses regarding Wi-Fi networks . In this work , the generalized network formulation is applied to the problem of radiation from arrays of flange-mounted rectangular and elliptical waveguides covered with a spherically stratified dielectric layer . It is shown that , by expressing the field in the half-space in terms of spherical waves , the physically matched solutions provide , in some cases , advantages for numerical computations and for network representations . This paper focuses on neural networks with complex-valued ( CV ) neurons as well as on selected aspects of neural networks learning , pruning and rule extraction . CV neurons can be used as versatile substitutes in real-valued perceptron networks . Furthermore , based on VDRW , we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks , where context sampling uses VDRW and the limited label information to create node-context pairs , and balanced-batch sampling adopts a simple under-sampling method to balance these pairs from different classes . Experimental results demonstrate that ImVerde based on VDRW outperforms state-of-the-art algorithms for learning network representations from imbalanced data . This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations . Although deep neural networks have exhibited superior performance in various tasks , interpretability is always Achilles ’ heel of deep neural networks . Representation learning of networks via embeddings has garnered popularity and has witnessed significant progress recently . Such representations have been effectively used for classic network-based machine learning tasks like link prediction , community detection , and network alignment . A Scalable Multiplex Network Embedding Framework . Representation learning of networks has witnessed significant progress in recent times . Such representations have been effectively used for classic network-based machine learning tasks like node classification , link prediction , and network alignment . Uniquely , this tutorial aims to provide the audience with the underlying theories in network representation learning , as well as our experience in translating this line of research into real-world applications on the Web . Finally , we will release public datasets and benchmarks for open and reproducible network representation learning research . The tutorial accompanying page is at https://aminer.org/nrl_www2019 . Computationally , the major phases of node2vec are trivially parallelizable , and it can scale to large networks with millions of nodes in a few hours . Overall our paper makes the following contributions : We propose node2vec , an efficient scalable algorithm for feature learning in networks that efficiently optimizes a novel network - aware , neighborhood preserving objective using SGD . We show how node2vec is in accordance with established principles in network science , providing flexibility in discovering representations conforming to different equivalences . Abstract In this work we make use of unsupervised linear discriminant analysis ( LDA ) to support acoustic unit discovery in a zero resource scenario . The idea is to automatically find a mapping of feature vectors into a subspace that is more suitable for Dirichlet process Gaussian mixture model ( DPGMM ) based clustering , without the need of supervision . We present an unsupervised algorithm for the discovery of words and word-like fragments from the speech signal , without using an upfront defined lexicon or acoustic phone models . The algorithm is based on a combination of acoustic pattern discovery , clustering , and temporal sequence learning . It exploits the acoustic similarity between multiple acoustic tokens of the same words or word-like fragments . This paper focuses research on acoustic modeling unit selection in Chinese Putonghua spontaneous speech recognition . Under HMM three-state models , two most popular modeling units , namely extended initial/final(XIF ) units and phoneme units , have their own advantages and drawbacks . These separate levels are , however , often addressed independently from one another . This thesis hopes to remedy that problem and presents a modular deep neural network for acoustic unit classification that can combine multiple well trained feature extraction networks into its topology . A word prediction deep neural network is also presented that functions at the lower subword level . Recent work on Acoustic Unit Discovery ( AUD ) has led to the development of a non-parametric Bayesian phone-loop model where the prior over the probability of the phone-like units is assumed to be sampled from a Dirichlet Process ( DP ) . In this work , we propose to improve this model by incorporating a Hierarchical Pitman-Yor based bigram Language Model on top of the units ' transitions . A compression method is proposed that takes advantage of a powerful property of acoustic unit inventories : In the appropriate acoustic space , units that share a ( context-dependent or -independent ) phoneme label must be close to a vector phoneme template associated with the phoneme . The method approximates units by interpolation between templates . This paper discusses pronunciation quality scoring for single syllable word in Putonghua Shuiping Ceshi ( PSC ) that is a nationwide spoken test to evaluate the standard level and the practical ability that an individual uses the mandarin in china . This study mainly includes some algorithms about the syllable separation , acoustic units selection , posterior probability scoring , threshold values setting and neural network combination . Experiment shows that the proposed approach achieves a high correlation of 0.731 , a value very close to 0.786 between human experts . In this paper , we explore the use of large-scale acoustic unit discovery for language recognition . The deep neural network-based approaches that have achieved recent success in this task require transcribed speech and pronunciation dictionaries , which may be limited in availability and expensive to obtain . Recently , there has been an increasing interest in learning features from spatio-temporal , binocular , or other multi-observation data , where the goal is to encode the relationship between images rather than the content of a single image . We provide an analysis of multi-view feature learning , which shows that hidden variables encode transformations by detecting rotation angles in the eigenspaces shared among multiple image warps . Our analysis helps explain recent experimental results showing that transformation-specific features emerge when training complex cell models on videos . We also extend VCCA with improved latent variable priors and with adversarial learning . Compared to other techniques for multi-view feature learning , VCCA 's advantages include an intuitive latent variable interpretation and a variational lower bound objective that can be trained end-to-end efficiently . We compare VCCA and its extensions with previous feature learning methods on the University of Wisconsin X-ray Microbeam Database , and show that VCCA-based feature learning improves over previous methods for speaker-independent phonetic recognition . Further , we also provide an important perspective from manifold alignment for multi-view representation learning . Overall , this survey aims to provide an insightful overview of theoretical basis and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications . We guide 3D shape descriptors toward discriminative representations by feeding heat distributions throughout time as inputs to units of heat diffusion LSTM ( HD-LSTM ) blocks with a supervised learning structure . We further extend HD-LSTM to a cross-domain structure ( CDHD-LSTM ) for learning domain-invariant representations of multi-view data . We evaluate the effectiveness of both HD-LSTM and CDHD-LSTM on 3D shape retrieval and sketch-based 3D shape retrieval tasks respectively . While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques , how to make the representation discriminative with only a small amount of labeled data is not well-studied . We introduce a semi-supervised neural network model , named Multi-view Discriminative Neural Network ( MDNN ) , for multi-view problems . Learning salient representations of multiview data is an essential step in many applications such as image classification , retrieval , and annotation . Standard predictive methods , such as support vector machines , often directly use all the features available without taking into consideration the presence of distinct views and the resultant view dependencies , coherence , and complementarity that offer key insights to the semantics of the data , and are therefore offering weak performance and are incapable of supporting view-level analysis . For multi-view data representation learning , recently the traditional unsupervised CCA method has been converted to supervised ways by introducing label information from samples . However , such supervised CCA variants require large numbers of labeled samples which hampers its practical application . In this paper , we propose a novel Multi-set Locality-Preserving Canonical Correlation Analysis ( MLPCCA ) for multi-view learning and fusion . The proposed MLPCCA captures the intrinsic structure of data while it learns the optimum basis for maximizing the correlation among different sets of data . In this paper , we consider a general problem of multiple view learning from pairwise constraints in the form of must-link and cannot-link . As one kind of side information , the must-link constraints imply that a pair of instances belongs to the same class , while the cannot-link constraints compel them to be different classes . This results in accurate performance in the presence of occlusion or other complex noise processes . We also investigate an extension of this idea for supervised multi-view learning where we develop a Bayesian multiple kernel learning algorithm that can learn a local weighting over each view of the input space . We additionally consider the problem of distributed object recognition or indexing from multiple cameras , where the computational power available at each camera sensor is limited and communication between cameras is prohibitively expensive . In the past few years , several backbone networks , e.g. , , have made significant advances in numerous vision tasks with state - of - the - art performance . Earlier architectures such as AlexNet and VGGNet stack convolutional operators , making the data - driven learning of multi - scale features feasible . The efficiency of multi - scale ability was subsequently improved by using conv layers with different kernel size ( e.g. , InceptionNets ) , residual modules ( e.g. , ResNet ) , shortcut connections ( e.g. , DenseNet ) , and hierarchical layer aggregation ( e.g. , DLA ) .
This paper summarizes ongoing research on a framework for representation learning using harmonic analysis , a subfield of mathematics . Harmonic analysis includes Fourier analysis , where new eigenvector representations are constructed by diagonalization of operators , and wavelet analysis , where new representations are constructed by dilation .
In contrast to autoencoder , which reconstructs the input data itself , neighbor-encoder reconstructs the input data 's neighbors . As the proposed representation learning problem is essentially a neighbor reconstruction problem , domain knowledge can be easily incorporated in the form of an appropriate definition of similarity between objects . Based on that observation , our framework can leverage any off-the-shelf similarity search algorithms or side information to find the neighbor of an input object .
The paper presents an original approach to music emotion recognition . We propose to use recurrent neural networks to separate the representation learning process from the classifier , which allows us to use a Support Vector Machine on top of a network to improve the results . We define a suitable loss function that is able to find a feature space in which similarity between vectors representing the music recordings corresponds to the similarity between their annotations .
This can be accomplished using dimensionality reduction , clustering , or graph - based techniques . In our work , we draw on recent developments in representation learning with neural networks , as discussed in the following section . However , even with a good learned state representation , maintaining a table of visitation frequencies becomes impractical for complex tasks .
‘ Network engineering ’ ’ is increasingly more important for visual recognition research . In this paper , we aim to develop new path topology of deep architectures to further push the frontier of representation learning . In particular , we focus on analyzing and reforming the skip connection , which has been widely used in designing modern deep neural networks and offers remarkable success in many applications .
kumar2015ask employ a structured neural network with episodic memory modules for natural language and also visual question answering . Similar to the above work , we leverage memory and attention in a recurrent neural network for inducing relations between tokens as a module in a larger network responsible for representation learning . As a property of soft attention , all intermediate relations we aim to capture are soft and differentiable .
In contrast to traditional handdesigned features ( e.g. , SIFT [ reference ] and HOG [ reference ] ) , features learned by neural networks from large - scale data [ reference ] require minimal human involvement during training , and can be transferred to a variety of recognition tasks [ reference ][ reference ][ reference ] . Nevertheless , human effort has been shifted to designing better network architectures for learning representations . Designing architectures becomes increasingly difficult with the growing number of hyper - parameters ( width [ reference ] , filter sizes , strides , etc . ) , especially when there are many layers .
Recent efforts sidestep this limitation through retrieving users ’ locations by alternatively unifying user generated contents ( e.g. , texts and public profiles ) and online social relations . Though achieving some progress , previous methods rely on the similarity of texts and/or neighboring nodes for user geolocation , which suffers the problems of : ( 1 ) location-agnostic problem of network representation learning , which largely impedes the performance of their prediction accuracy ; and ( 2 ) lack of interpretability w.r.t . the predicted results that is crucial for understanding model behavior and further improving prediction performance .
However , there exists a lack of algorithm of using the future evolution results of the networks to guide the network representation learning . Therefore , this paper aims at modeling the future network evolution results of the networks based on the link prediction algorithm , introducing the future link probabilities between vertices without edges into the network representation learning tasks . In order to make the network representation vectors contain more feature factors , the text features of the vertices are also embedded into the network representation vectors .
This is achieved by collecting multiple measurement points for each network and displaying them on a map . Our approach produces more accurate and detailed network representations on a map compared to existing applications on the market . Possible extensions to the current application include various analyses regarding Wi-Fi networks .
In this work , the generalized network formulation is applied to the problem of radiation from arrays of flange-mounted rectangular and elliptical waveguides covered with a spherically stratified dielectric layer . It is shown that , by expressing the field in the half-space in terms of spherical waves , the physically matched solutions provide , in some cases , advantages for numerical computations and for network representations .
This paper focuses on neural networks with complex-valued ( CV ) neurons as well as on selected aspects of neural networks learning , pruning and rule extraction . CV neurons can be used as versatile substitutes in real-valued perceptron networks .
Furthermore , based on VDRW , we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks , where context sampling uses VDRW and the limited label information to create node-context pairs , and balanced-batch sampling adopts a simple under-sampling method to balance these pairs from different classes . Experimental results demonstrate that ImVerde based on VDRW outperforms state-of-the-art algorithms for learning network representations from imbalanced data .
This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations . Although deep neural networks have exhibited superior performance in various tasks , interpretability is always Achilles ’ heel of deep neural networks .
Representation learning of networks via embeddings has garnered popularity and has witnessed significant progress recently . Such representations have been effectively used for classic network-based machine learning tasks like link prediction , community detection , and network alignment .
A Scalable Multiplex Network Embedding Framework . Representation learning of networks has witnessed significant progress in recent times . Such representations have been effectively used for classic network-based machine learning tasks like node classification , link prediction , and network alignment .
Uniquely , this tutorial aims to provide the audience with the underlying theories in network representation learning , as well as our experience in translating this line of research into real-world applications on the Web . Finally , we will release public datasets and benchmarks for open and reproducible network representation learning research . The tutorial accompanying page is at https://aminer.org/nrl_www2019 .
Computationally , the major phases of node2vec are trivially parallelizable , and it can scale to large networks with millions of nodes in a few hours . Overall our paper makes the following contributions : We propose node2vec , an efficient scalable algorithm for feature learning in networks that efficiently optimizes a novel network - aware , neighborhood preserving objective using SGD . We show how node2vec is in accordance with established principles in network science , providing flexibility in discovering representations conforming to different equivalences .
Abstract In this work we make use of unsupervised linear discriminant analysis ( LDA ) to support acoustic unit discovery in a zero resource scenario . The idea is to automatically find a mapping of feature vectors into a subspace that is more suitable for Dirichlet process Gaussian mixture model ( DPGMM ) based clustering , without the need of supervision .
We present an unsupervised algorithm for the discovery of words and word-like fragments from the speech signal , without using an upfront defined lexicon or acoustic phone models . The algorithm is based on a combination of acoustic pattern discovery , clustering , and temporal sequence learning . It exploits the acoustic similarity between multiple acoustic tokens of the same words or word-like fragments .
This paper focuses research on acoustic modeling unit selection in Chinese Putonghua spontaneous speech recognition . Under HMM three-state models , two most popular modeling units , namely extended initial/final(XIF ) units and phoneme units , have their own advantages and drawbacks .
These separate levels are , however , often addressed independently from one another . This thesis hopes to remedy that problem and presents a modular deep neural network for acoustic unit classification that can combine multiple well trained feature extraction networks into its topology . A word prediction deep neural network is also presented that functions at the lower subword level .
Recent work on Acoustic Unit Discovery ( AUD ) has led to the development of a non-parametric Bayesian phone-loop model where the prior over the probability of the phone-like units is assumed to be sampled from a Dirichlet Process ( DP ) . In this work , we propose to improve this model by incorporating a Hierarchical Pitman-Yor based bigram Language Model on top of the units ' transitions .
A compression method is proposed that takes advantage of a powerful property of acoustic unit inventories : In the appropriate acoustic space , units that share a ( context-dependent or -independent ) phoneme label must be close to a vector phoneme template associated with the phoneme . The method approximates units by interpolation between templates .
This paper discusses pronunciation quality scoring for single syllable word in Putonghua Shuiping Ceshi ( PSC ) that is a nationwide spoken test to evaluate the standard level and the practical ability that an individual uses the mandarin in china . This study mainly includes some algorithms about the syllable separation , acoustic units selection , posterior probability scoring , threshold values setting and neural network combination . Experiment shows that the proposed approach achieves a high correlation of 0.731 , a value very close to 0.786 between human experts .
In this paper , we explore the use of large-scale acoustic unit discovery for language recognition . The deep neural network-based approaches that have achieved recent success in this task require transcribed speech and pronunciation dictionaries , which may be limited in availability and expensive to obtain .
Recently , there has been an increasing interest in learning features from spatio-temporal , binocular , or other multi-observation data , where the goal is to encode the relationship between images rather than the content of a single image . We provide an analysis of multi-view feature learning , which shows that hidden variables encode transformations by detecting rotation angles in the eigenspaces shared among multiple image warps . Our analysis helps explain recent experimental results showing that transformation-specific features emerge when training complex cell models on videos .
We also extend VCCA with improved latent variable priors and with adversarial learning . Compared to other techniques for multi-view feature learning , VCCA 's advantages include an intuitive latent variable interpretation and a variational lower bound objective that can be trained end-to-end efficiently . We compare VCCA and its extensions with previous feature learning methods on the University of Wisconsin X-ray Microbeam Database , and show that VCCA-based feature learning improves over previous methods for speaker-independent phonetic recognition .
Further , we also provide an important perspective from manifold alignment for multi-view representation learning . Overall , this survey aims to provide an insightful overview of theoretical basis and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications .
We guide 3D shape descriptors toward discriminative representations by feeding heat distributions throughout time as inputs to units of heat diffusion LSTM ( HD-LSTM ) blocks with a supervised learning structure . We further extend HD-LSTM to a cross-domain structure ( CDHD-LSTM ) for learning domain-invariant representations of multi-view data . We evaluate the effectiveness of both HD-LSTM and CDHD-LSTM on 3D shape retrieval and sketch-based 3D shape retrieval tasks respectively .
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques , how to make the representation discriminative with only a small amount of labeled data is not well-studied . We introduce a semi-supervised neural network model , named Multi-view Discriminative Neural Network ( MDNN ) , for multi-view problems .
Learning salient representations of multiview data is an essential step in many applications such as image classification , retrieval , and annotation . Standard predictive methods , such as support vector machines , often directly use all the features available without taking into consideration the presence of distinct views and the resultant view dependencies , coherence , and complementarity that offer key insights to the semantics of the data , and are therefore offering weak performance and are incapable of supporting view-level analysis .
For multi-view data representation learning , recently the traditional unsupervised CCA method has been converted to supervised ways by introducing label information from samples . However , such supervised CCA variants require large numbers of labeled samples which hampers its practical application .
In this paper , we propose a novel Multi-set Locality-Preserving Canonical Correlation Analysis ( MLPCCA ) for multi-view learning and fusion . The proposed MLPCCA captures the intrinsic structure of data while it learns the optimum basis for maximizing the correlation among different sets of data .
In this paper , we consider a general problem of multiple view learning from pairwise constraints in the form of must-link and cannot-link . As one kind of side information , the must-link constraints imply that a pair of instances belongs to the same class , while the cannot-link constraints compel them to be different classes .
This results in accurate performance in the presence of occlusion or other complex noise processes . We also investigate an extension of this idea for supervised multi-view learning where we develop a Bayesian multiple kernel learning algorithm that can learn a local weighting over each view of the input space . We additionally consider the problem of distributed object recognition or indexing from multiple cameras , where the computational power available at each camera sensor is limited and communication between cameras is prohibitively expensive .
In the past few years , several backbone networks , e.g. , , have made significant advances in numerous vision tasks with state - of - the - art performance . Earlier architectures such as AlexNet and VGGNet stack convolutional operators , making the data - driven learning of multi - scale features feasible . The efficiency of multi - scale ability was subsequently improved by using conv layers with different kernel size ( e.g. , InceptionNets ) , residual modules ( e.g. , ResNet ) , shortcut connections ( e.g. , DenseNet ) , and hierarchical layer aggregation ( e.g. , DLA ) .
Representation Discovery in Planning using Harmonic Analysis Representation Learning by Reconstructing Neighborhoods Music emotion analysis using semantic embedding recurrent neural networks Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models Dual Path Networks Long Short-Term Memory-Networks for Machine Reading Aggregated Residual Transformations for Deep Neural Networks Multiple-Aspect Attentional Graph Neural Networks for Online Social Network User Localization Network representation based on the joint learning of three feature views Interaktivni zemljevid vstopnih točk Spherical Mode Expansions for Flange-mounted Waveguide Apertures Learning in networks: Complex-valued neurons, pruning, and rule extraction ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation Visual interpretability for deep learning: a survey MultiNet: Scalable Multilayer Network Embeddings Multi-Net: A Scalable Multiplex Network Embedding Framework Representation Learning on Networks: Theories, Algorithms, and Applications node2vec: Scalable Feature Learning for Networks Unsupervised Linear Discriminant Analysis for Supporting DPGMM Clustering in the Zero Resource Scenario A computational model for unsupervised word discovery Initial/final acoustic model based on separating nasal coda in Chinese Putonghua speech recognition Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition Bayesian phonotactic Language Model for Acoustic Unit Discovery Compression of acoustic inventories using asynchronous interpolation Pronunciation Quality Scoring for Single Syllable Word in PSC On the Use of Acoustic Unit Discovery for Language Recognition On multi-view feature learning Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods Heat Diffusion Long-Short Term Memory Learning for 3D Shape Analysis Semi-supervised Deep Representation Learning for Multi-View Problems Large-Margin Predictive Latent Subspace Learning for Multiview Data Analysis Sparse regularized discriminative canonical correlation analysis for multi-view semi-supervised learning Multiview emotion recognition via multi-set locality preserving canonical correlation analysis Multiple View Locality Preserving Projections with Pairwise Constraints Probabilistic models for multi-view semi-supervised learning and coding Res2Net: A New Multi-scale Backbone Architecture
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AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development INISTA ArXiv NIPS EMNLP 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) BigData COMPLEX NETWORKS (2) WWW (Companion Volume) KDD SLTU INTERSPEECH ICASSP ECCV (7) BigData ISCAS IEEE transactions on pattern analysis and machine intelligence
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