The connection between MLE and residuals is given before giving the introduction to M-estimator , which help in better understanding the M-estimator and robust regression . Then , it describes robust sparse PCA which can be efficiently used in feature learning . Lastly , it gives some applications about the robust sparse PCA which can help reader to understand it more intuitively . The efficiency of the proposed method is verified by electrocardiography ( ECG ) signal classification . The experiments show promising results over other feature based learning approaches . To tackle these problems , in this paper , we propose a novel deep cross-modal hashing method , called Adversary Guided Asymmetric Hashing ( AGAH ) . Specifically , it employs an adversarial learning guided multi-label attention module to enhance the feature learning part which can learn discriminative feature representations and keep the cross-modal invariability . Furthermore , in order to generate hash codes which can fully preserve the multi-label semantics of all items , we propose an asymmetric hashing method which utilizes a multi-label binary code map that can equip the hash codes with multi-label semantic information . The big data trend has inspired feature-driven learning tasks , which can not be handled by conventional machine learning models . Unstructured data produces very large binary matrices with millions of columns when converted to vector form . [ reference ] using stochastic gradient ascent over the model parameters defining the features . Feature learning methods based on the Skip - gram architecture have been originally developed in the context of natural language . Given the linear nature of text , the notion of a neighborhood can be naturally defined using a sliding window over consecutive words . Each surrogate class is formed by applying a variety of transformations to a randomly sampled ’ seed ’ image patch . We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition . The feature representation learned by our algorithm achieves classification results matching or outperforming the current state - of - the - art for unsupervised learning on several popular datasets ( STL - 10 , CIFAR - 10 , Caltech - 101 ) . Based on the analysis , we propose a simple dual path architecture which shares the across all blocks to enjoy the benefits of reusing common features with low redundancy , while still remaining a densely connected path to give the network more flexibility in learning new features . We formulate such a dual path architecture as follows : where and denote the extracted information at - th step from individual path , is a feature learning function as . Eqn . In addition , the presence of vanishing gradients also makes the DL training slow and ineffective . In this paper , we address the feature learning problem in DL by presenting a new algorithm , deeply - supervised nets ( DSN ) , which enforces direct and early supervision for both the hidden layers and the output layer . We introduce companion objective to the individual hidden layers , which is used as an additional constraint ( or a new regularization ) to the learning process . It can also be used with a variety of loss functions , including differentiable supervised losses and nondifferentiable reinforcement learning objectives . The process of training a model 's parameters such that a few gradient steps , or even a single gradient step , can produce good results on a new task can be viewed from a feature learning standpoint as building an internal representation that is broadly suitable for many tasks . If the internal representation is suitable to many tasks , simply fine - tuning the parameters slightly ( e.g. by primarily modifying the top layer weights in a feedforward model ) can produce good results . As revealed in , the interactions with useless features may introduce noises and degrade the performance . In recent years , deep neural networks ( DNNs ) have become successful in computer vision , speech recognition , and natural language processing with their great power of feature representation learning . It is promising to exploit DNNs to learn sophisticated and selective feature interactions . Recently , deep feature learning based methods , which learn a global pedestrian feature and use Euclidean metric to measure two samples , have obtained the state - of - the - art results . With the increasing sample size of ReID dataset , the learning of features from multi - class person identification tasks , denoted as ID - discriminative Embedding ( IDE ) , has shown great potentials on current large - scale person ReID datasets , such as MARS and PRW , where the IDE features are taken from the last hidden layer of Deep Convolutional Neural Networks ( DCNN ) . In this paper , we aim to learn the IDE feature for person ReID using DCNN . ( d ) , if more and more global context is integrated into the classificiation process , the computer case next to the monitor can be easily misclassified as a monitor due to the similar appearance . Thus , it is significant to explicitly involve the semantic boundary to guide the learning of the features . It can amplify the variation of features on both sides . After the recent developments in Artificial Neural Networks and deep learning techniques , representation learning has become the focus of many research interests . In the field of Natural Language Processing , representation learning techniques have gained many implementation advances and improved different tasks compared to any other method . Learning the representations of nodes in a network can benefit various analysis tasks such as node classification , link prediction , clustering , and anomaly detection . Such a representation learning problem is referred to as network embedding , and it has attracted significant attention in recent years . In this article , we briefly review the existing network embedding methods by two taxonomies . Recently , it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest . The developed representation learning process is based on a cascade of cascades of decision tree forests , where the high memory requirement and the high time cost inhibit the training of large models . In this paper , we propose a simple yet effective approach to improve the efficiency of deep forest . Early methods consider only simple topological features , while subsequent supervised approaches typically rely on human-labeled data and feature engineering . In this work , we present a new representation learning-based approach called SEMAC that jointly exploits fine-grained node features as well as the overall graph topology . In contrast to the SGNS or SVD methods espoused in previous representation-based studies , our model represents nodes in terms of subgraph embeddings acquired via a form of convex matrix completion to iteratively reduce the rank , and thereby , more effectively eliminate noise in the representation . The success in handwritten digit recognition can be attributed to advances in machine-learning techniques . In the field of machine learning , representation-based learning in deep learning context is gaining popularity in the recent years . Representative deep learning methods have successfully implemented in image classification , action recognition , object tracking , etc . In this paper , we address this problem by developing representation learning techniques for business processes . More specifically , the representation learning paradigm is applied to activities , traces , logs , and models in order to learn highly informative but low-dimensional vectors , often referred to as embeddings , based on a neural network architecture . Subsequently , these vectors can be used for automated inference tasks such as trace clustering , process comparison , predictive process monitoring , anomaly detection , etc . So far , two lines of work have tried to produce a"good " feature vector x from the customer 's behavioraldata : ( 1 ) application-specific feature engineering using behavioral data and ( 2 ) representation learning ( such as singular value decomposition or neuralembedding ) on behavioral data . Although these approaches successfullyimprove the predictive performance , ( 1 ) designing a good feature requiresdomain experts to make a great effort and ( 2 ) features obtained fromrepresentation learning are hard to interpret . To overcome these problems , we present a Relational Infinite SupportVector Machine ( R-iSVM ) , a mixture-of-experts model that can leveragebehavioral data . This conversion preserves exponential forms of the original distribution and consequently conserves the original features of the data . We show how to solve this new representation learning formulation and illustrate it ’s favorable properties over the original IB when trained from a small sample . We also analyze the critical points of the dualIB and discuss their importance for the quality of this approach . As a result , the learned model is biased towards the synthetic data , which leads to an unsatisfactory segmentation performance on real urban scene images . From the perspective of representation learning , this is largely because that the convolutional filters in the semantic segmentation models overfit to the synthetic image style . Consequently , when taking a real image as input , the feature representation generated using the learned model might not be sufficiently informative for semantic segmentation , since the convolutional filters tend to extract information that look discriminative in synthetic style only . The model processes text incrementally while learning which past tokens in the memory and to what extent they relate to the current token being processed . As a result , the model induces undirected relations among tokens as an intermediate step of learning representations . We validate the performance of the LSTMN in language modeling , sentiment analysis , and natural language inference . A key idea behind the LSTMN is to use attention for inducing relations between tokens . These relations are soft and differentiable , and components of a larger representation learning network . Although it is appealing to provide direct supervision for the attention layer , e.g. , with evidence collected from a dependency treebank , we treat it as a submodule being optimized within the larger network in a downstream task . 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 . section : Conclusion This paper introduces a representation learning algorithm called Information Maximizing Generative Adversarial Networks ( InfoGAN ) . In contrast to previous approaches , which require supervision , InfoGAN is completely unsupervised and learns interpretable and disentangled representations on challenging datasets . Our main contribution is to give what is , to the best of our knowledge , the first generalization - error bound for estimating individual - level causal effect , where each individual is identified by its features . The bound leads naturally to a new family of representation - learning based algorithms , which we show to match or outperform state - of - the - art methods on several causal effect inference tasks . We frame our results using the Rubin - Neyman potential outcomes framework , as follows . Such pre - training methods perform unsupervised representation learning on a large corpus of unlabeled data followed by supervised training . A key disadvantage of pre - training is that the first representation learning phase does not take advantage of labeled data – the model attempts to learn generally effective representations rather than ones that are targeted towards a particular task . Older semi - supervised learning algorithms like self - training do not suffer from this problem because they continually learn about a task on a mix of labeled and unlabeled data . Then design a model of the heterogeneous network integration gateway between IEEE802.15.4a and Profibus-DP . It includes the designs of intermediate layer and mapping mechanism , and in detail describes the virtual station data structure which can indicate the data change of wireless terminal station , and the mapping relationship between this data structure and wireless stations . ( [ reference ] ) is a hyperbolic tangent function . The computation from the hidden state in the decoder to the output is implemented as a deep neural network with a single intermediate layer having 500 maxout units each pooling 2 inputs . All the weight parameters in the RNN Encoder – Decoder were initialized by sampling from an isotropic zero - mean ( white ) Gaussian distribution with its standard deviation fixed to , except for the recurrent weight parameters . Once this space has been produced , tasks such as face recognition , verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors . Our method uses a deep convolutional network trained to directly optimize the embedding itself , rather than an intermediate bottleneck layer as in previous deep learning approaches . To train , we use triplets of roughly aligned matching / non - matching face patches generated using a novel online triplet mining method . This can be viewed as a full - resolution counterpart to the CRN , based on dilating the filters instead of scaling the feature maps . One of the drawbacks of this approach is that all intermediate feature layers are at full image resolution and have a high memory footprint . Thus the ratio of capacity ( number of parameters ) to memory footprint is much lower than in the CRN . subsection : ELMo ELMo is a task specific combination of the intermediate layer representations in the biLM . For each token , a - layer biLM computes a set of representations where is the token layer and , for each biLSTM layer . The classification is carried out using the back propagation neural network , which is a network with hidden layer . The added advantage of hidden layer for lip reading is that it takes into account the nonlinear variation of lip features while speaking . The proposed approach is used for Hindi word recognition and achieved high accuracy at the cost of computation time . The accuracy of prediction algorithms have been measured by mean square error ( MSE ) . The experiment showed that the best hidden layer architecture ( HLA ) is 5 - 10 - 11 - 12 - 13 - 1 with learning function ( LF ) of trainlm , activation function ( AF ) of logsig and purelin , and learning rate ( LR ) of 0.5 . This architecture has a good accuracy with MSE of 0.0643 . Authors proposed a solution to the problem of resource authorization and authentication by introducing a Hidden Data Layer based on steganographic methods to SDN . Hidden Data Layer consists of three essential elements , i.e. a Head Resident which doubles the functions of a public SDN controller and is a superior unit concerning public one , a Hidden Layer Protocol realising communication between individual network elements . The third element is Hidden Protocol Interpreter as an interpretive and executive function which carries out tasks sent by secret layer protocol . Learning is accomplished by providing sets of connected input / output units where each connection has a weight associated with it . Further Hidden layer 1 helps to extract data to estimate the forecasting equation and also it helps for evaluating estimated Model using different measures . To improve the performance of the network , special care has been taken into consideration for handling Hidden layer 2 which is used for generation of forecasts based on estimated model and again forecasts are evaluated ; if it is bad , model need to be changed or revised that is how BP comes into the picture in order to handle such uncertainty . We used the same setup in the language inference task except that we replace the layer with a layer and model the following conditional probability distribution . where and are the question and the answer encoded vectors and denotes the output of the hidden layer of the MLP . For this task , we use NTI - SLSTM - LSTM to encode answer candidate sentences and NTI - ANF - LSTM to encode the question sentences . The GCN model is motivated by a localized first - order approximation of spectral convolutions for layer - wise information propagation on graphs . Similar to our LoNGAE model , the GCN model can learn hidden layer representations that encode both local graph structure and features of nodes . The choice of the decoder depends on the task . As baselines , we train RNN and LSTM models on the symbolic data in raw sequence form . The RNNs and LSTMs use 50 dimensional embeddings and 50 dimensional hidden layers ; they predict a single output at the end of the sequences and the output is treated as a classification problem , the loss is cross entropy . The RNNs and LSTMs contain around 5k and 30k parameters , respectively . PNN : For the purpose of capturing high - order feature interactions , PNN imposes a product layer between the embedding layer and the first hidden layer . According to different types of product operation , there are three variants : IPNN , OPNN , and PNN , where IPNN is based on inner product of vectors , OPNN is based on outer product , and PNN is based on both inner and outer products . More specifically , their method represents the parameter matrix as a product of two low rank factors , and the training algorithm fixes one factor ( called static parameters ) and only updates the other factor ( called dynamic parameters ) . uses low - rank matrix factorization to reduce the size of the fully connected layers at train time . They demonstrate large improvements in reducing the number of parameters of the output softmax layer , but only modest improvements for the hidden fully connected layers . implements low - rank factorizations using the SVD after training the full model . A similar but a different concept of supervising intermediate layers for a convolutional network is used in Lee et al . Their method simultaneously minimizes classification error while improving the directness and transparency of the hidden layer learning process . There are two significant differences between our recursive - supervision and deep - supervision proposed in Lee et al . . The required up - sampling of the low - resolution features was performed with simple repetition in our experiments . This was found sufficient , with the following hidden layers learning to combine the multi - scale features . In the case of architectures with strides greater than unary , the last convolutional layers of the two pathways , and , have receptive fields and with strides and respectively . Note that all of these results are still lower than BM25 , which shows that they are not good at learning from weak supervision signals for ranking . We have also examined the score model with a network with a single linear hidden layer , with the embedding vector representation , which is equivalent to a linear regression model with the ability of representation learning . Comparing the results of this experiment with Score - Embed in Table [ reference ] , we can see that with a single - linear network we are not able to achieve a performance that is as good as a deep neural network with non - linearity . The exact feature function is parser dependent and will be discussed when discussing the corresponding parsers . The resulting feature vectors are then scored using a non - linear function , namely a multi - layer perceptron with one hidden layer ( MLP ) : where are the model parameters . Beside using the BiLSTM - based feature functions , we make use of standard parsing techniques . Presently , the DNS implementations all run on top of IPv4 addresses and the DNS system that supports IPv6 is linked to IPv4 information . Some DNS implementations are beginning to support national IPv6 transitions , such as , bind8 with a KAME patch , or newbie and bind9 ( which are still under development ) . Upgrading these servers is a crucial step in the conversion process . Thus , both machine learning and computational neuroscience are faced with a shared challenge : how can we analyze their representations in order to understand how they solve complex tasks ? We review how data-analysis concepts and techniques developed by computational neuroscientists can be useful for analyzing representations in DNNs , and in turn , how recently developed techniques for analysis of DNNs can be useful for understanding representations in biological neural networks . We explore opportunities for synergy between the two fields , such as the use of DNNs as in silico model systems for neuroscience , and how this synergy can lead to new hypotheses about the operating principles of biological neural networks . While DNNs deliver state-of-the-art accuracy on many AI tasks , it comes at the cost of high computational complexity . Accordingly , techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI system . An up-to-date overview is provided on four deep learning architectures , namely , Artificial Neural Networks , Auto encoder , convolutional neural network , deep belief network , and restricted Boltzmann machine , Recurrent Neural Network . DNS consists or three rield elements : they are -1 , 0 , and + 1 . Since DNS representation is not necessarily unique , new algorithms to generate the number or representations and all equivalent representations are presented in this paper . A Block-Oriented-Computer ( BOC ) is developed as an array or computing systems . In this work , we investigate cross-lingual BN features in hybrid ASR systems under a two-level DNNs framework . The firstlevel DNNs are bottleneck feature extractors and the secondlevel DNNs serve as not only acoustic models but also feature combination modules . Different feature configurations , including the bottleneck dimensionality , the need of delta processing and the necessity of concatenation with standard features of target-language , are first studied . However , due to the limited hardware resources , severe IR-Drop and process variation problems for synapse crossbar , and limited synapse device resolution , it ’s still a great challenge for hardware NCS design to catch up with the fast development of software deep neural networks ( DNNs ) . This dissertation explores model compression and acceleration methods for deep neural networks to save both memory and computation resources for the hardware implementation of DNNs . Firstly , DNNs ’ weights quantization work is presented to use three orthogonal methods to learn synapses with one-level precision , namely , distribution-aware quantization , quantization regularization and bias tuning , to make image classification accuracy comparable to the state-ofthe-art . Understanding and characterizing the subspaces of adversarial examples aid in studying the robustness of deep neural networks ( DNNs ) to adversarial perturbations . Very recently , Ma et al. ( ICLR 2018 ) proposed to use local intrinsic dimensionality ( LID ) in layer-wise hidden representations of DNNs to study adversarial subspaces . It was demonstrated that LID can be used to characterize the adversarial subspaces associated with different attack methods , e.g. , the Carlini and Wagner 's ( C&W ) attack and the fast gradient sign attack . Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU , we reveal that DAN/K-DAN are trainable using only CPU even for small-scale training sets . Our experimental results show that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs , including multiplayer perceptron , deep belief network , etc . , without data augmentation applied . Visualizing Deep Representations . Our work is also related to the recent efforts in visualizing the internal representations of DNNs . One popular approach uses gradient - based optimization to find an image whose representation is close to the one we want to visualize . For solving the problem , we propose a two-phase classification model . In a first phase , we train independent classifiers from image data , using a deep learning representation , and from text data , using a bag-of-words representation . During testing we apply a cascade fusion of the classifiers . Recently , deep learning-based representation learning methods have attracted increasing attention in machine fault diagnosis . However , few existing methods consider the geometry of data samples . Feature extraction is significantly important to the radar target recognition based on the HRRP . Conventional deep models of representation learning used for HRRP target recognition commonly use the vanilla autoen-coder and deep belief net ( DBN ) , moreover , the simulated HRRP samples used in these related work are mostly under the free space condition which is different from the real world situation . In this paper , convolution architecture autoencoder , which is more efficient in spatial feature extraction and sparse coding , is proposed . Abstract : We have developed what is , to the best of our knowledge , the first complete theory of representation for decision and control task , which has shown not only to encompass and explain all known phenomenology in deep neural network-based representation learning , but also to predict phenomena that were thus far unexplained . Preference aggregation , and specifically rank aggregation , is a well known problem in the fields of computational social choice and preference handling with broad application including web search and recommendation systems . Inspired by the recent advances in the area of deep neural representation learning , for the first time in the literature , in this paper we leverage unsupervised deep learning techniques - especially graph embeddings - for aggregating a collection of incomplete rank lists and accordingly we develop an algorithm called DeepAggregation . It takes as input a set of incomplete rank lists and constructs a multi-layer graph wherein the nodes are the alternatives that are ranked and the edges capture information contained in the incomplete rank lists . section : Abstract - We propose a deep learning method for single image super - resolution ( SR ) . Our method directly learns an end - to - end mapping between the low / high - resolution images . In , authors propose various architectures based on VGG network to employ covariance pooling . In , authors present a deep learning architecture for learning on Riemannian manifold which can be employed for covariance pooling . subsection : Facial Expression Recognition from Videos Below , we focus on the harder unsupervised case , although the proposed approach can be generalized to the semi - supervised case rather straightforwardly . Unlike most previous papers on domain adaptation that worked with fixed feature representations , we focus on combining domain adaptation and deep feature learning within one training process ( deep domain adaptation ) . Our goal is to embed domain adaptation into the process of learning representation , so that the final classification decisions are made based on features that are both discriminative and invariant to the change of domains , i.e. have the same or very similar distributions in the source and the target domains . section : Conclusion This paper have shown that the effect of the face identification and verification supervisory signals on deep feature representation coincide with the two aspects of constructing ideal features for face recognition , _ meaning : NTF . i.e Related works on discriminative model include . By combining the deep learning algorithm , the discriminative scheme has achieved substantial improvement on AIFR . For example , Wen et al . extended the HFA method to a deep CNN model called latent factor guided convolutional neural networks ( LF - CNNs ) , which achieved the state - of - the - art recognition accuracy in this field . The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses , which is a correlation structure associated with the unique dynamics of CANNs . This body of evidence , which is reviewed in this report , suggests that CANNs may serve as a canonical model for neural information representation . Model-based methods require a precise calibration and suffer from high order non-linearity . While , neural-based representations does not require a dedicated calibration process to solve these tasks . However , some suffer from high generalization error that reduces the accuracy or require large-scale data collection . The similarity between the two sets of data indicates that discrimination between spatial-frequency gratings is probably based on the separation between two features exactly one cycle apart . We suggest that the segmentation reflects the existence of neural-image representations with discrete levels of spatial accuracy . Common coding proposes a shared representation in the brain between perception , execution , and imagination of movement . This neural-level representation is considered to support a “ direct activation ” of action by perception/imagination of movement at the behavioral level . We examine how this two-tier model , where a representation at the neural level supports “ direct activation ” at the behavioral level , relates to the notion of situatedness — the real-time access and use of environment structure for cognition . After several evolutionary generations , the network develops systematically regular mappings between meanings and sequences , of the sort traditionally associated with symbolic grammars . Because of the potential of neural-like representations for addressing the symbol-grounding problem , this sort of model holds a good deal of promise as a new explanatory mechanism for both language evolution and acquisition . The general approach of achieving domain adaptation explored under many facets . Over the years , a large part of the literature has focused mainly on linear hypothesis ( see for instance [ reference ][ reference ][ reference ][ reference ] . More recently , non - linear representations have become increasingly studied , including neural network representations [ reference ] and most notably the state - of - the - art mSDA [ reference ] .
The connection between MLE and residuals is given before giving the introduction to M-estimator , which help in better understanding the M-estimator and robust regression . Then , it describes robust sparse PCA which can be efficiently used in feature learning . Lastly , it gives some applications about the robust sparse PCA which can help reader to understand it more intuitively .
The efficiency of the proposed method is verified by electrocardiography ( ECG ) signal classification . The experiments show promising results over other feature based learning approaches .
To tackle these problems , in this paper , we propose a novel deep cross-modal hashing method , called Adversary Guided Asymmetric Hashing ( AGAH ) . Specifically , it employs an adversarial learning guided multi-label attention module to enhance the feature learning part which can learn discriminative feature representations and keep the cross-modal invariability . Furthermore , in order to generate hash codes which can fully preserve the multi-label semantics of all items , we propose an asymmetric hashing method which utilizes a multi-label binary code map that can equip the hash codes with multi-label semantic information .
The big data trend has inspired feature-driven learning tasks , which can not be handled by conventional machine learning models . Unstructured data produces very large binary matrices with millions of columns when converted to vector form .
[ reference ] using stochastic gradient ascent over the model parameters defining the features . Feature learning methods based on the Skip - gram architecture have been originally developed in the context of natural language . Given the linear nature of text , the notion of a neighborhood can be naturally defined using a sliding window over consecutive words .
Each surrogate class is formed by applying a variety of transformations to a randomly sampled ’ seed ’ image patch . We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition . The feature representation learned by our algorithm achieves classification results matching or outperforming the current state - of - the - art for unsupervised learning on several popular datasets ( STL - 10 , CIFAR - 10 , Caltech - 101 ) .
Based on the analysis , we propose a simple dual path architecture which shares the across all blocks to enjoy the benefits of reusing common features with low redundancy , while still remaining a densely connected path to give the network more flexibility in learning new features . We formulate such a dual path architecture as follows : where and denote the extracted information at - th step from individual path , is a feature learning function as . Eqn .
In addition , the presence of vanishing gradients also makes the DL training slow and ineffective . In this paper , we address the feature learning problem in DL by presenting a new algorithm , deeply - supervised nets ( DSN ) , which enforces direct and early supervision for both the hidden layers and the output layer . We introduce companion objective to the individual hidden layers , which is used as an additional constraint ( or a new regularization ) to the learning process .
It can also be used with a variety of loss functions , including differentiable supervised losses and nondifferentiable reinforcement learning objectives . The process of training a model 's parameters such that a few gradient steps , or even a single gradient step , can produce good results on a new task can be viewed from a feature learning standpoint as building an internal representation that is broadly suitable for many tasks . If the internal representation is suitable to many tasks , simply fine - tuning the parameters slightly ( e.g. by primarily modifying the top layer weights in a feedforward model ) can produce good results .
As revealed in , the interactions with useless features may introduce noises and degrade the performance . In recent years , deep neural networks ( DNNs ) have become successful in computer vision , speech recognition , and natural language processing with their great power of feature representation learning . It is promising to exploit DNNs to learn sophisticated and selective feature interactions .
Recently , deep feature learning based methods , which learn a global pedestrian feature and use Euclidean metric to measure two samples , have obtained the state - of - the - art results . With the increasing sample size of ReID dataset , the learning of features from multi - class person identification tasks , denoted as ID - discriminative Embedding ( IDE ) , has shown great potentials on current large - scale person ReID datasets , such as MARS and PRW , where the IDE features are taken from the last hidden layer of Deep Convolutional Neural Networks ( DCNN ) . In this paper , we aim to learn the IDE feature for person ReID using DCNN .
( d ) , if more and more global context is integrated into the classificiation process , the computer case next to the monitor can be easily misclassified as a monitor due to the similar appearance . Thus , it is significant to explicitly involve the semantic boundary to guide the learning of the features . It can amplify the variation of features on both sides .
After the recent developments in Artificial Neural Networks and deep learning techniques , representation learning has become the focus of many research interests . In the field of Natural Language Processing , representation learning techniques have gained many implementation advances and improved different tasks compared to any other method .
Learning the representations of nodes in a network can benefit various analysis tasks such as node classification , link prediction , clustering , and anomaly detection . Such a representation learning problem is referred to as network embedding , and it has attracted significant attention in recent years . In this article , we briefly review the existing network embedding methods by two taxonomies .
Recently , it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest . The developed representation learning process is based on a cascade of cascades of decision tree forests , where the high memory requirement and the high time cost inhibit the training of large models . In this paper , we propose a simple yet effective approach to improve the efficiency of deep forest .
Early methods consider only simple topological features , while subsequent supervised approaches typically rely on human-labeled data and feature engineering . In this work , we present a new representation learning-based approach called SEMAC that jointly exploits fine-grained node features as well as the overall graph topology . In contrast to the SGNS or SVD methods espoused in previous representation-based studies , our model represents nodes in terms of subgraph embeddings acquired via a form of convex matrix completion to iteratively reduce the rank , and thereby , more effectively eliminate noise in the representation .
The success in handwritten digit recognition can be attributed to advances in machine-learning techniques . In the field of machine learning , representation-based learning in deep learning context is gaining popularity in the recent years . Representative deep learning methods have successfully implemented in image classification , action recognition , object tracking , etc .
In this paper , we address this problem by developing representation learning techniques for business processes . More specifically , the representation learning paradigm is applied to activities , traces , logs , and models in order to learn highly informative but low-dimensional vectors , often referred to as embeddings , based on a neural network architecture . Subsequently , these vectors can be used for automated inference tasks such as trace clustering , process comparison , predictive process monitoring , anomaly detection , etc .
So far , two lines of work have tried to produce a"good " feature vector x from the customer 's behavioraldata : ( 1 ) application-specific feature engineering using behavioral data and ( 2 ) representation learning ( such as singular value decomposition or neuralembedding ) on behavioral data . Although these approaches successfullyimprove the predictive performance , ( 1 ) designing a good feature requiresdomain experts to make a great effort and ( 2 ) features obtained fromrepresentation learning are hard to interpret . To overcome these problems , we present a Relational Infinite SupportVector Machine ( R-iSVM ) , a mixture-of-experts model that can leveragebehavioral data .
This conversion preserves exponential forms of the original distribution and consequently conserves the original features of the data . We show how to solve this new representation learning formulation and illustrate it ’s favorable properties over the original IB when trained from a small sample . We also analyze the critical points of the dualIB and discuss their importance for the quality of this approach .
As a result , the learned model is biased towards the synthetic data , which leads to an unsatisfactory segmentation performance on real urban scene images . From the perspective of representation learning , this is largely because that the convolutional filters in the semantic segmentation models overfit to the synthetic image style . Consequently , when taking a real image as input , the feature representation generated using the learned model might not be sufficiently informative for semantic segmentation , since the convolutional filters tend to extract information that look discriminative in synthetic style only .
The model processes text incrementally while learning which past tokens in the memory and to what extent they relate to the current token being processed . As a result , the model induces undirected relations among tokens as an intermediate step of learning representations . We validate the performance of the LSTMN in language modeling , sentiment analysis , and natural language inference .
A key idea behind the LSTMN is to use attention for inducing relations between tokens . These relations are soft and differentiable , and components of a larger representation learning network . Although it is appealing to provide direct supervision for the attention layer , e.g. , with evidence collected from a dependency treebank , we treat it as a submodule being optimized within the larger network in a downstream task .
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 .
section : Conclusion This paper introduces a representation learning algorithm called Information Maximizing Generative Adversarial Networks ( InfoGAN ) . In contrast to previous approaches , which require supervision , InfoGAN is completely unsupervised and learns interpretable and disentangled representations on challenging datasets .
Our main contribution is to give what is , to the best of our knowledge , the first generalization - error bound for estimating individual - level causal effect , where each individual is identified by its features . The bound leads naturally to a new family of representation - learning based algorithms , which we show to match or outperform state - of - the - art methods on several causal effect inference tasks . We frame our results using the Rubin - Neyman potential outcomes framework , as follows .
Such pre - training methods perform unsupervised representation learning on a large corpus of unlabeled data followed by supervised training . A key disadvantage of pre - training is that the first representation learning phase does not take advantage of labeled data – the model attempts to learn generally effective representations rather than ones that are targeted towards a particular task . Older semi - supervised learning algorithms like self - training do not suffer from this problem because they continually learn about a task on a mix of labeled and unlabeled data .
Then design a model of the heterogeneous network integration gateway between IEEE802.15.4a and Profibus-DP . It includes the designs of intermediate layer and mapping mechanism , and in detail describes the virtual station data structure which can indicate the data change of wireless terminal station , and the mapping relationship between this data structure and wireless stations .
( [ reference ] ) is a hyperbolic tangent function . The computation from the hidden state in the decoder to the output is implemented as a deep neural network with a single intermediate layer having 500 maxout units each pooling 2 inputs . All the weight parameters in the RNN Encoder – Decoder were initialized by sampling from an isotropic zero - mean ( white ) Gaussian distribution with its standard deviation fixed to , except for the recurrent weight parameters .
Once this space has been produced , tasks such as face recognition , verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors . Our method uses a deep convolutional network trained to directly optimize the embedding itself , rather than an intermediate bottleneck layer as in previous deep learning approaches . To train , we use triplets of roughly aligned matching / non - matching face patches generated using a novel online triplet mining method .
This can be viewed as a full - resolution counterpart to the CRN , based on dilating the filters instead of scaling the feature maps . One of the drawbacks of this approach is that all intermediate feature layers are at full image resolution and have a high memory footprint . Thus the ratio of capacity ( number of parameters ) to memory footprint is much lower than in the CRN .
subsection : ELMo ELMo is a task specific combination of the intermediate layer representations in the biLM . For each token , a - layer biLM computes a set of representations where is the token layer and , for each biLSTM layer .
The classification is carried out using the back propagation neural network , which is a network with hidden layer . The added advantage of hidden layer for lip reading is that it takes into account the nonlinear variation of lip features while speaking . The proposed approach is used for Hindi word recognition and achieved high accuracy at the cost of computation time .
The accuracy of prediction algorithms have been measured by mean square error ( MSE ) . The experiment showed that the best hidden layer architecture ( HLA ) is 5 - 10 - 11 - 12 - 13 - 1 with learning function ( LF ) of trainlm , activation function ( AF ) of logsig and purelin , and learning rate ( LR ) of 0.5 . This architecture has a good accuracy with MSE of 0.0643 .
Authors proposed a solution to the problem of resource authorization and authentication by introducing a Hidden Data Layer based on steganographic methods to SDN . Hidden Data Layer consists of three essential elements , i.e. a Head Resident which doubles the functions of a public SDN controller and is a superior unit concerning public one , a Hidden Layer Protocol realising communication between individual network elements . The third element is Hidden Protocol Interpreter as an interpretive and executive function which carries out tasks sent by secret layer protocol .
Learning is accomplished by providing sets of connected input / output units where each connection has a weight associated with it . Further Hidden layer 1 helps to extract data to estimate the forecasting equation and also it helps for evaluating estimated Model using different measures . To improve the performance of the network , special care has been taken into consideration for handling Hidden layer 2 which is used for generation of forecasts based on estimated model and again forecasts are evaluated ; if it is bad , model need to be changed or revised that is how BP comes into the picture in order to handle such uncertainty .
We used the same setup in the language inference task except that we replace the layer with a layer and model the following conditional probability distribution . where and are the question and the answer encoded vectors and denotes the output of the hidden layer of the MLP . For this task , we use NTI - SLSTM - LSTM to encode answer candidate sentences and NTI - ANF - LSTM to encode the question sentences .
The GCN model is motivated by a localized first - order approximation of spectral convolutions for layer - wise information propagation on graphs . Similar to our LoNGAE model , the GCN model can learn hidden layer representations that encode both local graph structure and features of nodes . The choice of the decoder depends on the task .
As baselines , we train RNN and LSTM models on the symbolic data in raw sequence form . The RNNs and LSTMs use 50 dimensional embeddings and 50 dimensional hidden layers ; they predict a single output at the end of the sequences and the output is treated as a classification problem , the loss is cross entropy . The RNNs and LSTMs contain around 5k and 30k parameters , respectively .
PNN : For the purpose of capturing high - order feature interactions , PNN imposes a product layer between the embedding layer and the first hidden layer . According to different types of product operation , there are three variants : IPNN , OPNN , and PNN , where IPNN is based on inner product of vectors , OPNN is based on outer product , and PNN is based on both inner and outer products .
More specifically , their method represents the parameter matrix as a product of two low rank factors , and the training algorithm fixes one factor ( called static parameters ) and only updates the other factor ( called dynamic parameters ) . uses low - rank matrix factorization to reduce the size of the fully connected layers at train time . They demonstrate large improvements in reducing the number of parameters of the output softmax layer , but only modest improvements for the hidden fully connected layers . implements low - rank factorizations using the SVD after training the full model .
A similar but a different concept of supervising intermediate layers for a convolutional network is used in Lee et al . Their method simultaneously minimizes classification error while improving the directness and transparency of the hidden layer learning process . There are two significant differences between our recursive - supervision and deep - supervision proposed in Lee et al . .
The required up - sampling of the low - resolution features was performed with simple repetition in our experiments . This was found sufficient , with the following hidden layers learning to combine the multi - scale features . In the case of architectures with strides greater than unary , the last convolutional layers of the two pathways , and , have receptive fields and with strides and respectively .
Note that all of these results are still lower than BM25 , which shows that they are not good at learning from weak supervision signals for ranking . We have also examined the score model with a network with a single linear hidden layer , with the embedding vector representation , which is equivalent to a linear regression model with the ability of representation learning . Comparing the results of this experiment with Score - Embed in Table [ reference ] , we can see that with a single - linear network we are not able to achieve a performance that is as good as a deep neural network with non - linearity .
The exact feature function is parser dependent and will be discussed when discussing the corresponding parsers . The resulting feature vectors are then scored using a non - linear function , namely a multi - layer perceptron with one hidden layer ( MLP ) : where are the model parameters . Beside using the BiLSTM - based feature functions , we make use of standard parsing techniques .
Presently , the DNS implementations all run on top of IPv4 addresses and the DNS system that supports IPv6 is linked to IPv4 information . Some DNS implementations are beginning to support national IPv6 transitions , such as , bind8 with a KAME patch , or newbie and bind9 ( which are still under development ) . Upgrading these servers is a crucial step in the conversion process .
Thus , both machine learning and computational neuroscience are faced with a shared challenge : how can we analyze their representations in order to understand how they solve complex tasks ? We review how data-analysis concepts and techniques developed by computational neuroscientists can be useful for analyzing representations in DNNs , and in turn , how recently developed techniques for analysis of DNNs can be useful for understanding representations in biological neural networks . We explore opportunities for synergy between the two fields , such as the use of DNNs as in silico model systems for neuroscience , and how this synergy can lead to new hypotheses about the operating principles of biological neural networks .
While DNNs deliver state-of-the-art accuracy on many AI tasks , it comes at the cost of high computational complexity . Accordingly , techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI system . An up-to-date overview is provided on four deep learning architectures , namely , Artificial Neural Networks , Auto encoder , convolutional neural network , deep belief network , and restricted Boltzmann machine , Recurrent Neural Network .
DNS consists or three rield elements : they are -1 , 0 , and + 1 . Since DNS representation is not necessarily unique , new algorithms to generate the number or representations and all equivalent representations are presented in this paper . A Block-Oriented-Computer ( BOC ) is developed as an array or computing systems .
In this work , we investigate cross-lingual BN features in hybrid ASR systems under a two-level DNNs framework . The firstlevel DNNs are bottleneck feature extractors and the secondlevel DNNs serve as not only acoustic models but also feature combination modules . Different feature configurations , including the bottleneck dimensionality , the need of delta processing and the necessity of concatenation with standard features of target-language , are first studied .
However , due to the limited hardware resources , severe IR-Drop and process variation problems for synapse crossbar , and limited synapse device resolution , it ’s still a great challenge for hardware NCS design to catch up with the fast development of software deep neural networks ( DNNs ) . This dissertation explores model compression and acceleration methods for deep neural networks to save both memory and computation resources for the hardware implementation of DNNs . Firstly , DNNs ’ weights quantization work is presented to use three orthogonal methods to learn synapses with one-level precision , namely , distribution-aware quantization , quantization regularization and bias tuning , to make image classification accuracy comparable to the state-ofthe-art .
Understanding and characterizing the subspaces of adversarial examples aid in studying the robustness of deep neural networks ( DNNs ) to adversarial perturbations . Very recently , Ma et al. ( ICLR 2018 ) proposed to use local intrinsic dimensionality ( LID ) in layer-wise hidden representations of DNNs to study adversarial subspaces . It was demonstrated that LID can be used to characterize the adversarial subspaces associated with different attack methods , e.g. , the Carlini and Wagner 's ( C&W ) attack and the fast gradient sign attack .
Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU , we reveal that DAN/K-DAN are trainable using only CPU even for small-scale training sets . Our experimental results show that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs , including multiplayer perceptron , deep belief network , etc . , without data augmentation applied .
Visualizing Deep Representations . Our work is also related to the recent efforts in visualizing the internal representations of DNNs . One popular approach uses gradient - based optimization to find an image whose representation is close to the one we want to visualize .
For solving the problem , we propose a two-phase classification model . In a first phase , we train independent classifiers from image data , using a deep learning representation , and from text data , using a bag-of-words representation . During testing we apply a cascade fusion of the classifiers .
Recently , deep learning-based representation learning methods have attracted increasing attention in machine fault diagnosis . However , few existing methods consider the geometry of data samples .
Feature extraction is significantly important to the radar target recognition based on the HRRP . Conventional deep models of representation learning used for HRRP target recognition commonly use the vanilla autoen-coder and deep belief net ( DBN ) , moreover , the simulated HRRP samples used in these related work are mostly under the free space condition which is different from the real world situation . In this paper , convolution architecture autoencoder , which is more efficient in spatial feature extraction and sparse coding , is proposed .
Abstract : We have developed what is , to the best of our knowledge , the first complete theory of representation for decision and control task , which has shown not only to encompass and explain all known phenomenology in deep neural network-based representation learning , but also to predict phenomena that were thus far unexplained .
Preference aggregation , and specifically rank aggregation , is a well known problem in the fields of computational social choice and preference handling with broad application including web search and recommendation systems . Inspired by the recent advances in the area of deep neural representation learning , for the first time in the literature , in this paper we leverage unsupervised deep learning techniques - especially graph embeddings - for aggregating a collection of incomplete rank lists and accordingly we develop an algorithm called DeepAggregation . It takes as input a set of incomplete rank lists and constructs a multi-layer graph wherein the nodes are the alternatives that are ranked and the edges capture information contained in the incomplete rank lists .
section : Abstract - We propose a deep learning method for single image super - resolution ( SR ) . Our method directly learns an end - to - end mapping between the low / high - resolution images .
In , authors propose various architectures based on VGG network to employ covariance pooling . In , authors present a deep learning architecture for learning on Riemannian manifold which can be employed for covariance pooling . subsection : Facial Expression Recognition from Videos
Below , we focus on the harder unsupervised case , although the proposed approach can be generalized to the semi - supervised case rather straightforwardly . Unlike most previous papers on domain adaptation that worked with fixed feature representations , we focus on combining domain adaptation and deep feature learning within one training process ( deep domain adaptation ) . Our goal is to embed domain adaptation into the process of learning representation , so that the final classification decisions are made based on features that are both discriminative and invariant to the change of domains , i.e. have the same or very similar distributions in the source and the target domains .
section : Conclusion This paper have shown that the effect of the face identification and verification supervisory signals on deep feature representation coincide with the two aspects of constructing ideal features for face recognition , _ meaning : NTF . i.e
Related works on discriminative model include . By combining the deep learning algorithm , the discriminative scheme has achieved substantial improvement on AIFR . For example , Wen et al . extended the HFA method to a deep CNN model called latent factor guided convolutional neural networks ( LF - CNNs ) , which achieved the state - of - the - art recognition accuracy in this field .
The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses , which is a correlation structure associated with the unique dynamics of CANNs . This body of evidence , which is reviewed in this report , suggests that CANNs may serve as a canonical model for neural information representation .
Model-based methods require a precise calibration and suffer from high order non-linearity . While , neural-based representations does not require a dedicated calibration process to solve these tasks . However , some suffer from high generalization error that reduces the accuracy or require large-scale data collection .
The similarity between the two sets of data indicates that discrimination between spatial-frequency gratings is probably based on the separation between two features exactly one cycle apart . We suggest that the segmentation reflects the existence of neural-image representations with discrete levels of spatial accuracy .
Common coding proposes a shared representation in the brain between perception , execution , and imagination of movement . This neural-level representation is considered to support a “ direct activation ” of action by perception/imagination of movement at the behavioral level . We examine how this two-tier model , where a representation at the neural level supports “ direct activation ” at the behavioral level , relates to the notion of situatedness — the real-time access and use of environment structure for cognition .
After several evolutionary generations , the network develops systematically regular mappings between meanings and sequences , of the sort traditionally associated with symbolic grammars . Because of the potential of neural-like representations for addressing the symbol-grounding problem , this sort of model holds a good deal of promise as a new explanatory mechanism for both language evolution and acquisition .
The general approach of achieving domain adaptation explored under many facets . Over the years , a large part of the literature has focused mainly on linear hypothesis ( see for instance [ reference ][ reference ][ reference ][ reference ] . More recently , non - linear representations have become increasingly studied , including neural network representations [ reference ] and most notably the state - of - the - art mSDA [ reference ] .
Robust Sparse Representation, Modeling and Learning A New Supervised Learning Approach: Statistical Adaptive Fourier Decomposition (SAFD) Adversary Guided Asymmetric Hashing for Cross-Modal Retrieval Comparison of Classification Methods for Very High-Dimensional Data in Sparse Random Projection Representation node2vec: Scalable Feature Learning for Networks Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Dual Path Networks Deeply-Supervised Nets Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification Learning a Discriminative Feature Network for Semantic Segmentation Compositional representations of language structures in multilingual joint-vector space A Brief Review of Network Embedding Improving Deep Forest by Confidence Screening Link Prediction via Subgraph Embedding-Based Convex Matrix Completion A Study of Representation Learning for Handwritten Numeral Recognition of Multilingual Data Set act2vec, trace2vec, log2vec, and model2vec: Representation Learning for Business Processes Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data The Dual Information Bottleneck ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Long Short-Term Memory-Networks for Machine Reading node2vec: Scalable Feature Learning for Networks InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Estimating individual treatment effect: generalization bounds and algorithms Semi-Supervised Sequence Modeling with Cross-View Training Gateway Architecture Design of Wire/Wireless Heterogeneous Network Integration Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation FaceNet: A unified embedding for face recognition and clustering Photographic Image Synthesis with Cascaded Refinement Networks Deep contextualized word representations Investigating back propagation neural network for lip reading Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach SDN implementation in regard to network elements security Backpropagation Network for Prediction of Foreign Exchange Rates Correlated with Case Based Reasoning (CBR) Neural Tree Indexers for Text Understanding Learning to Make Predictions on Graphs with Autoencoders Gated Graph Sequence Neural Networks DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Deep Fried Convnets Deeply-Recursive Convolutional Network for Image Super-Resolution Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Neural Ranking Models with Weak Supervision Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations The adoption of IPv6 Analyzing biological and artificial neural networks: challenges with opportunities for synergy? Review Based on Different Deep Learning Architecture and their Application The dinary numerical system and its applications to modern parallel processing computers Investigation of cross-lingual bottleneck features in hybrid ASR systems Efficient and Robust Neuromorphic Computing Design On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification Stacked Generative Adversarial Networks User Identification in Pinterest Through the Refinement of Cascade Fusion of Text and Images Learning Representations With Local and Global Geometries Preserved for Machine Fault Diagnosis Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition Analysis-Driven Design of Representations For Sensing-Action Systems DeepAggregation: A New Approach for Aggregating Incomplete Ranked Lists using Multi-Layer Graph Embedding Image Super-Resolution Using Deep Convolutional Networks Covariance Pooling for Facial Expression Recognition Unsupervised Domain Adaptation by Backpropagation Deep Learning Face Representation by Joint Identification-Verification Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation Humanoid Robot Grasping with a Soft Gripper Through a Learned Inverse Model of a Central Pattern Generator and Tactile Servoing Limits of spatial-frequency discrimination as evidence of neural interpolation. Rethinking Situatedness Evolving Distributed Representations for Language with Self-Organizing Maps Domain-Adversarial Training of Neural Networks
9c1a9f58c2a9bd64ef2ac9a1065a07487a749362 9a7f20508fce69eab9e1fa57bb4d392a365027ba 14dce0850544fe5ed2b635f2b1b1dfba720d0a3e 4b003ce17806434ba3c75156245d51cd9acd1df4 49dcc3ae8387cebf78d684b975f2e37d19e8bc65 df84fff20c6183cb47b3c6da2ec984749cb1c21b 801e587e974a8764378600f7a270404b879e3acd c1a7b09e469c23d48bfe9f991f59fed23a5fc16a 417d36a4771432ff61b15f48dfc668d83c1a8b3f 4d6adfbadd3353dfcafdbfbc856b1efefca99daf 1862f222d2cafec005a3753fafb7f815d8f3c0ac 7420a4a01ec844432db034f959c6823a72e12b68 c6c6e1fc911b7e2ff09b8258df884f8cb6ddc027 b48775f02f354af337227c58f71c25769b5435a0 e9690b0b7da437399eeb2232ae6b79f339d540f5 28663862fd456706666254f883002be3ea22bda1 16d1128f5b405c2a96bfd55ff32705f4a9e2f130 cb6a1654b0bb14cc098306f976e172e6f5f8ddaa 807bd132892517eff403de8998c85118f1903fa5 8206694e424e03fde268759de2b28974e6337352 28aa2060b2170590404f5cc93b161c737cc25658 b17f4e84193d15c13d4037b41c394b4fbc25ff32 ae64b2e2681f20706203e161b3f414e233bbd74d 29f712156b5c216fe00c2ec8fa115bdfcce6bbf3 c34ea19b42236a516ab7044a8eb1fced7ec1847f 6b4b48b62d1dfad86203702293018d0b42fd0ce0 27e351f40ea8882e3c33af522913cc8c1373fae7 d3f00abd64b665a691afee170432648e7a180e5d 6c3b6a6d3fd97000a829408211845303dc3ae9e1 1abd1efae8c3849e28de926e52d166b7800965a1 9bd6f45364067c8ebf296fc374087f8f7f797fa4 7ecd598a78cdfa2ee131e6f56411a31e8273d2a2 6ff62594f12c524d68aba56149fe73554b5213ab c397beab38a9dc5687f60eb939d841924b920a42 3290b24851391886b9819045f4a61a643c298247
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Medicine","Biology","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science","Medicine","Mathematics"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Biology","Medicine","Computer Science"] ["Computer Science"] ["Physics","Medicine"] ["Psychology"] ["Computer Science"]
2015 2019 2019 2018 2016 2014 2017 2015 2017 2018 2017 2018 2019 2019 2018 2018 2018 2018 2017 2019 2018 2016 2016 2016 2017 2018 2009 2014 2015 2017 2018 2016 2019 2019 2014 2017 2018 2016 2017 2015 2016 2017 2017 2016 2003 2018 2018 1970 2014 2020 2018 2019 2017 2017 2020 2018 2017 2019 2016 2018 2015 2014 2018 2016 2018 1982 2010 2006 2016
ICONIP (5) ICMR ELM KDD NIPS NIPS AISTATS ICML KDD 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICDM AAAI BPM ICDM 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition EMNLP KDD NIPS ICML EMNLP EMNLP 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) NAACL-HLT EACL 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) ICLR IJCAI 2015 IEEE International Conference on Computer Vision (ICCV) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Medical Image Anal. SIGIR Transactions of the Association for Computational Linguistics INTERSPEECH 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AAMAS IEEE Transactions on Pattern Analysis and Machine Intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ICML NIPS ECCV Humanoids EELC J. Mach. Learn. Res.
https://api.semanticscholar.org/9c1a9f58c2a9bd64ef2ac9a1065a07487a749362 https://api.semanticscholar.org/9a7f20508fce69eab9e1fa57bb4d392a365027ba https://api.semanticscholar.org/14dce0850544fe5ed2b635f2b1b1dfba720d0a3e https://api.semanticscholar.org/4b003ce17806434ba3c75156245d51cd9acd1df4 https://www.semanticscholar.org/paper/36ee2c8bd605afd48035d15fdc6b8c8842363376 https://www.semanticscholar.org/paper/a6fd96a900d4130940b488863b71fd09ad41ccb9 https://www.semanticscholar.org/paper/d7ddad7bbda29de7676c21bfeac6be2ce0a07d6f https://www.semanticscholar.org/paper/fb91db6aa4f710814f8aec28a7f3ecbc4e5ad4fd https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://www.semanticscholar.org/paper/3448e6a5039417dc1ae890efeca3bef5390ace7c https://www.semanticscholar.org/paper/605886b07ec27492e8d5bc48f79ddc39c02ec190 https://www.semanticscholar.org/paper/343a128beb49db94169f24ea6c8cb22d6d59ecc4 https://api.semanticscholar.org/49dcc3ae8387cebf78d684b975f2e37d19e8bc65 https://api.semanticscholar.org/df84fff20c6183cb47b3c6da2ec984749cb1c21b https://api.semanticscholar.org/801e587e974a8764378600f7a270404b879e3acd https://api.semanticscholar.org/c1a7b09e469c23d48bfe9f991f59fed23a5fc16a https://api.semanticscholar.org/417d36a4771432ff61b15f48dfc668d83c1a8b3f https://api.semanticscholar.org/4d6adfbadd3353dfcafdbfbc856b1efefca99daf https://api.semanticscholar.org/1862f222d2cafec005a3753fafb7f815d8f3c0ac https://api.semanticscholar.org/7420a4a01ec844432db034f959c6823a72e12b68 https://www.semanticscholar.org/paper/0301963f73e8ace21653c0bed0e5feee128f140e https://www.semanticscholar.org/paper/13fe71da009484f240c46f14d9330e932f8de210 https://www.semanticscholar.org/paper/36ee2c8bd605afd48035d15fdc6b8c8842363376 https://www.semanticscholar.org/paper/35da0a2001eea88486a5de677ab97868c93d0824 https://www.semanticscholar.org/paper/17409ae85c237983dc22a5e4750d8054e4b6edc9 https://www.semanticscholar.org/paper/0c47cad9729c38d9db1f75491b1ee4bd883a5d4e https://api.semanticscholar.org/c6c6e1fc911b7e2ff09b8258df884f8cb6ddc027 https://www.semanticscholar.org/paper/0b544dfe355a5070b60986319a3f51fb45d1348e https://www.semanticscholar.org/paper/5aa26299435bdf7db874ef1640a6c3b5a4a2c394 https://www.semanticscholar.org/paper/28eceb438da0b841bbd3d02684dbfa263838ed60 https://www.semanticscholar.org/paper/3febb2bed8865945e7fddc99efd791887bb7e14f https://api.semanticscholar.org/b48775f02f354af337227c58f71c25769b5435a0 https://api.semanticscholar.org/e9690b0b7da437399eeb2232ae6b79f339d540f5 https://api.semanticscholar.org/28663862fd456706666254f883002be3ea22bda1 https://api.semanticscholar.org/16d1128f5b405c2a96bfd55ff32705f4a9e2f130 https://www.semanticscholar.org/paper/705dcc8eadba137834e4b0359e2d696d4b209f5b https://www.semanticscholar.org/paper/2e4c06dd00c4c09ad5ac6be883cc66c19d88ea79 https://www.semanticscholar.org/paper/492f57ee9ceb61fb5a47ad7aebfec1121887a175 https://www.semanticscholar.org/paper/1d122a074c936fcfd95faf44608e377a9d1799c8 https://www.semanticscholar.org/paper/27a99c21a1324f087b2f144adc119f04137dfd87 https://www.semanticscholar.org/paper/06c06885fd53b2cbd407704cf14f658842ed48e5 https://www.semanticscholar.org/paper/7c2bcf6f32b05a04cd3444c030db743e5666af88 https://www.semanticscholar.org/paper/c7a59fc69aee480e53cd1f869bad4c83170d1328 https://www.semanticscholar.org/paper/eec3a236ecd185712ce65fb336141f8656eea13d https://api.semanticscholar.org/cb6a1654b0bb14cc098306f976e172e6f5f8ddaa https://api.semanticscholar.org/807bd132892517eff403de8998c85118f1903fa5 https://api.semanticscholar.org/8206694e424e03fde268759de2b28974e6337352 https://api.semanticscholar.org/28aa2060b2170590404f5cc93b161c737cc25658 https://api.semanticscholar.org/b17f4e84193d15c13d4037b41c394b4fbc25ff32 https://api.semanticscholar.org/ae64b2e2681f20706203e161b3f414e233bbd74d https://api.semanticscholar.org/29f712156b5c216fe00c2ec8fa115bdfcce6bbf3 https://api.semanticscholar.org/c34ea19b42236a516ab7044a8eb1fced7ec1847f https://www.semanticscholar.org/paper/9ec59d3b0320fc8abfaf297be45f0da6fdeef3d7 https://api.semanticscholar.org/6b4b48b62d1dfad86203702293018d0b42fd0ce0 https://api.semanticscholar.org/27e351f40ea8882e3c33af522913cc8c1373fae7 https://api.semanticscholar.org/d3f00abd64b665a691afee170432648e7a180e5d https://api.semanticscholar.org/6c3b6a6d3fd97000a829408211845303dc3ae9e1 https://api.semanticscholar.org/1abd1efae8c3849e28de926e52d166b7800965a1 https://www.semanticscholar.org/paper/66e9dc728b5041271bff0cd6ac0d7eadcd88442f https://www.semanticscholar.org/paper/0f0cab9235bbf185acdd4f9713fd111ca50effca https://www.semanticscholar.org/paper/2530cfc7764bda1330c48c0c8e2cd0e0c671d7e1 https://www.semanticscholar.org/paper/91bdaf3f1226e4065c4296d5c362906ceadfc631 https://www.semanticscholar.org/paper/269c7aeca29dae51dca8208815f1c4c81bd471c2 https://api.semanticscholar.org/9bd6f45364067c8ebf296fc374087f8f7f797fa4 https://api.semanticscholar.org/7ecd598a78cdfa2ee131e6f56411a31e8273d2a2 https://api.semanticscholar.org/6ff62594f12c524d68aba56149fe73554b5213ab https://api.semanticscholar.org/c397beab38a9dc5687f60eb939d841924b920a42 https://api.semanticscholar.org/3290b24851391886b9819045f4a61a643c298247 https://www.semanticscholar.org/paper/1d5972b32a9b5a455a6eef389de5b7fca25771ad