The fashion of generating masks leads to different learning methods , i.e. , DropOut , DropConnect . We propose a new method called DropPart which is a generalization of DropConnect . In DropPart the Beta distribution instead of Bernoulli distribution in DropConnect is used . While training with the two regularization methods , a randomly chosen subsets of activations/weights are dropped . Consequently , the assessment on the HACDB database to treat character level proves shows an improvement of classification error rate once adding Dropout and DropConnect techniques . In this work , we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network . While most of the works in literature impose $ \ell_1 $ regularization , DropOut or DropConnect techniques to induce sparsity , our scheme considers feature importance as a criterion to allocate the trainable parameters ( resources ) efficiently in the network . Even though sparsity is ensured , $ \ell_1 $ regularization requires training on all the resources in a deep neural network . We assign the basic unit of drop method for convolutional weights to be the whole kernel windows , so one output map value is dropped . We evaluated the proposed DropKernel strategy by the object classification performance on CIFAR10 in comparison to conventional Dropout and DropConnect methods , and showed improved performance of the proposed method . Such energy and bandwidth constraints arise for low-power operation in the communication between neural units , which cause dropped spike events due to timeout errors in the transmission . The Dropout and DropConnect processes during training of the network are aligned with a statistical model of the network during inference that accounts for these random errors in the transmission of neural states and synaptic connections . The use of Dropout and DropConnect during training hence allows to simultaneously meet two design objectives : improving robustness of inference to dropped spike events due to timeout communication constraints in network connectivity , while maximizing time-to-decision bandwidth and hence minimizing inference energy in the neuromorphic hardware . Motivated by the success of DropConnect , we extended this algorithm with the ability of sparse feature selection . In DropConnect algorithm , the dropping masks of weights are generated using Bernoulli gating variables that are independent of the weights and activations . We introduce a new strategy to generate masks depending on the outputs of previous layer . Using this method , neurons which are promising to produce sparser features will be assigned a bigger possibility to keep active in the forward and backward propagations . We then evaluate such sparsity constrained DropConnect on MNIST and CIFAR datasets in comparison with ordinary DropConnect and Dropout method . The results show that our new method improves the sparsity of features significantly , while not degrading the precision . Another way to look at it is the percentage learning evident in a mini-batch training epoch and thereafter removing the weight connections where a certain percentage of learning is not evident . Subsequently , the method is evaluated on several benchmark classification tasks with a drastically smaller size and better performance to models trained with other similar regularization technique of DropConnect . A filtering algorithm is used to learn a variational approximation to the evolving in time posterior over the weights . Training is pursued through a sequential version of Bayes by Backprop , which is enriched with a stronger regularization technique called variational DropConnect . The experiments are focused on streaming data and time series . Variational dropout ( ) uses the same dropout mask throughout a sequence during training . DropConnect applies the dropout operation on the weight matrices . Zoneout ( ) , in a similar spirit with dropout , randomly chooses to use the previous time step hidden state instead of using the current one . The first problem we tackle is low rank matrix learning which is a popular model assumption used in MTL . Trace norm regularization is a widely used approach for learning such models . A standard optimization strategy is based on formulating the problem as one of low rank matrix factorization which , however , leads to a non-convex problem . As compared with the convex regularization approaches ( say , L1 regularization ) , however , the convergence issue of the corresponding algorithms are more difficult to tackle . In this paper , we deal with this difficult issue for a specific but typical nonconvex regularization scheme , the L1/2 regularization , which has been successfully used to many applications . More specifically , we study the convergence of the iterative half thresholding algorithm ( the half algorithm for short ) , one of the most efficient and important algorithms for solution to the L1/2 regularization . Additionally , the well-known stochastic dual dynamic programming ( SDDP ) algorithm is customized and improved to drastically reduce the computation time and significantly improve solution quality when approximately solving this MSP model . Finally , and more generally , a novel nonconvex regularization scheme is developed to improve the computational performance of the SDDP algorithm for solving high-dimensional MSP models . Specifically , it is shown that these nonconvex regularization problems can be reformulated as mixed-integer programming problems with provable convergence guarantees . It is widely hypothesized that image registration is an essential component in such systems . MethodsThe two non-rigid registration methods are : ( 1 ) a deformable registration of the prostate segmentation distance maps with B-spline regularization and ( 2 ) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data . We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures . Here the newly developed method of determining the optimal regularization parameter α in uniform Tykhonov-Phillips regularization ( α-weighted S-homBLE ) by A-optimal design ( minimizing the trace of the Mean Square Error matrix MSE ) is reviewed . This new algorithm with A-optimal Regularization can be applied to overcome this kind problem in both GPS rapid static and real time kinematic positioning with single or dual frequency measurements , especially for the shorter period observation . In the case study , both the estimate methods are applied to process the two-epoch L1 data in single frequency GPS rapid static positioning . The ℓ1 regularization problem has been widely used to solve the sparsity constrained problems . To enhance the sparsity constraint for better imaging performance , a promising direction is to use the ℓp norm ( 0 < ; p < ; 1 ) and solve the ℓp minimization problem . In this paper , we propose a new $ \ell_{2,1}$-norm SVM , that is , multi-class hinge loss with a structured regularization term for all the classes to naturally select features for multi-class without bothering further heuristic strategy . Rather than directly solving the multi-class hinge loss with $ \ell_{2,1}$-norm regularization minimization , which has not been solved before due to its optimization difficulty , we are the first to give an efficient algorithm bridging the new problem with a previous solvable optimization problem to do multi-class feature selection . A global convergence proof for our method is also presented . As such , most of the computational results claimed in the biophysics literature are difficult to reproduce . In this note , we give an exposition of this method , connect it with some related ideas in the literature , and propose a modification of the original method based on replacing mesh smoothing with harmonic energy regularization . We present a theoretical finding and related computational observations explaining why such a smoothing or regularization step is indispensable for the success of the algorithm . Dynamic systems condition estimation regularization algorithms in the conditions of signals and hindrances statistical characteristics aprioristic uncertainty are offered . Regular iterative algorithms of strengthening matrix factor elements of the Kalman filter , allowing to adapt the filter to changing hindrance-alarm conditions are developed . Therefore , in this project we want to investigate benefits of applying machine learning classification methodologies to Internet advertisement problem based on published images . In order to gain further insight into the nature of image-based classification problem in the context of Internet advertisement , we apply various regularization and sparsity promoting techniques to determine the features that are more dominant in making the distinguishment between the two classes of images . Our model incorporates the Maximum Mean Discrepancy ( MMD ) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space . From experiments , we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set . We also show that our proposed model , preceded by the denoising auto-encoder pretraining , achieves better performance than recent benchmark models on the same data sets . i s are parameters specific to two consecutive labels y i−1 and y i . For training , standard log - likelihood loss is used with L 2 regularization given a set of gold - standard instances . section : Experiments p ( y = k|x ) of the model is most sensitive . [ reference ] and [ reference ] experimentally verified that the predictors trained with the standard regularization technique such as L 1 and L 2 regularization are likely to make mistakes when the signal is perturbed in the adversarial direction , even when the norm of the perturbation is so small that it can not be perceived by human eyes . Inspired by this finding , Goodfellow et al . We show that adversarial training can provide an additional regularization benefit beyond that provided by using dropout dropout alone . Generic regularization strategies such as dropout , pretraining , and model averaging do not confer a significant reduction in a model ’s vulnerability to adversarial examples , but changing to nonlinear model families such as RBF networks can do so . Our explanation suggests a fundamental tension between designing models that are easy to train due to their linearity and designing models that use nonlinear effects to resist adversarial perturbation . We therefore normalize through either projection or batch - normalization with fixed scale . We refer to the model minimizing ( [ reference ] ) with as Counterfactual Regression ( CFR ) and the variant without balance regularization ( ) as Treatment - Agnostic Representation Network ( TARNet ) . We train our models by minimizing ( [ reference ] ) using stochastic gradient descent , where we backpropagate the error through both the hypothesis and representation networks , as described in Algorithm [ reference ] . We consider the problem of classifying nodes ( such as documents ) in a graph ( such as a citation network ) , where labels are only available for a small subset of nodes . This problem can be framed as graph - based semi - supervised learning , where label information is smoothed over the graph via some form of explicit graph - based regularization zhu2003semi , zhou2004learning , belkin2006manifold , weston2012deep , e.g. by using a graph Laplacian regularization term in the loss function : Here , denotes the supervised loss w.r.t . the labeled part of the graph , can be a neural network - like differentiable function , is a weighing factor and is a matrix of node feature vectors . Revaud propose a method EpicFlow that uses externally matched flows as initialization and then performs interpolation . Zimmer design the complementary regularization that exploits directional information from the constraints imposed in data term . Our network that infers optical flow and performs flow regularization is inspired by the use of data fidelity and regularization in variational methods . Through analysis and experiments , we elucidate connections between FractalNet and an array of phenomena engineered into previous deep network designs . As an additional contribution , we develop drop - path , a novel regularization protocol for ultra - deep fractal networks . Without data augmentation , fractal networks , trained with drop - path and dropout dropout , exceed the performance of residual networks regularized via stochastic depth huang2016stochasticdepth . merity2016pointer based on perplexity . For fair comparison , we closely follow the regularization and optimization techniques introduced by merity2017regularizing . We heuristically and manually search hyper - parameters for MoS based on the validation performance while limiting the model size ( see Appendix [ reference ] for our hyper - parameters ) . The behavior of the algorithms is similar to the one exhibited by the noisy artificial data above ( medium quality of graphs ) . For most observation levels our method combining nuclear norm and graph regularization ( red line ) clearly outperforms the rest . There are however two boundary phases that are noteworthy . Here , regularization took several forms . First , in all layers , we bounded the absolute value of the kernel weights and applied both L1 and L2 regularization to prevent overfitting . This is done by adding the regularization terms to the negative log - probability ( i.e. , where and are coefficients for L1 and L2 regularization terms respectively ) . Unlike the existing methods where dropout is applied only to the non-recurrent connections , the proposed method applies dropout to the recurrent connections as well in such a way that RNNs generalize well . Our experiments show that rnnDrop is a better regularization method than others including weight noise injection . Namely , when deep bidirectional long short-term memory ( LSTM ) RNNs were trained with rnnDrop as acoustic models for phoneme and speech recognition , they significantly outperformed the current state-of-the-arts ; we achieved the phoneme error rate of 16.29 % on the TIMIT core test set for phoneme recognition and the word error rate of 5.53 % on the Wall Street Journal ( WSJ ) dataset , dev93 , for speech recognition , which are the best reported results on both of the datasets . We find that when appropriate forms of Dropout are applied in the right place in the model architecture that non-insignificant improvement in Mean Intersection over Union ( mIoU ) score can be observed . In our best case , we find that applying Dropout scheduling in conjunction with SpatialDropout improves baseline mIoU from 0.49 to 0.59 . This result shows that even where a model architecture makes extensive use of Batch Normalization , Dropout can still be an effective way of improving performance in low data situations .
The fashion of generating masks leads to different learning methods , i.e. , DropOut , DropConnect . We propose a new method called DropPart which is a generalization of DropConnect . In DropPart the Beta distribution instead of Bernoulli distribution in DropConnect is used .
While training with the two regularization methods , a randomly chosen subsets of activations/weights are dropped . Consequently , the assessment on the HACDB database to treat character level proves shows an improvement of classification error rate once adding Dropout and DropConnect techniques .
In this work , we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network . While most of the works in literature impose $ \ell_1 $ regularization , DropOut or DropConnect techniques to induce sparsity , our scheme considers feature importance as a criterion to allocate the trainable parameters ( resources ) efficiently in the network . Even though sparsity is ensured , $ \ell_1 $ regularization requires training on all the resources in a deep neural network .
We assign the basic unit of drop method for convolutional weights to be the whole kernel windows , so one output map value is dropped . We evaluated the proposed DropKernel strategy by the object classification performance on CIFAR10 in comparison to conventional Dropout and DropConnect methods , and showed improved performance of the proposed method .
Such energy and bandwidth constraints arise for low-power operation in the communication between neural units , which cause dropped spike events due to timeout errors in the transmission . The Dropout and DropConnect processes during training of the network are aligned with a statistical model of the network during inference that accounts for these random errors in the transmission of neural states and synaptic connections . The use of Dropout and DropConnect during training hence allows to simultaneously meet two design objectives : improving robustness of inference to dropped spike events due to timeout communication constraints in network connectivity , while maximizing time-to-decision bandwidth and hence minimizing inference energy in the neuromorphic hardware .
Motivated by the success of DropConnect , we extended this algorithm with the ability of sparse feature selection . In DropConnect algorithm , the dropping masks of weights are generated using Bernoulli gating variables that are independent of the weights and activations . We introduce a new strategy to generate masks depending on the outputs of previous layer . Using this method , neurons which are promising to produce sparser features will be assigned a bigger possibility to keep active in the forward and backward propagations . We then evaluate such sparsity constrained DropConnect on MNIST and CIFAR datasets in comparison with ordinary DropConnect and Dropout method . The results show that our new method improves the sparsity of features significantly , while not degrading the precision .
Another way to look at it is the percentage learning evident in a mini-batch training epoch and thereafter removing the weight connections where a certain percentage of learning is not evident . Subsequently , the method is evaluated on several benchmark classification tasks with a drastically smaller size and better performance to models trained with other similar regularization technique of DropConnect .
A filtering algorithm is used to learn a variational approximation to the evolving in time posterior over the weights . Training is pursued through a sequential version of Bayes by Backprop , which is enriched with a stronger regularization technique called variational DropConnect . The experiments are focused on streaming data and time series .
Variational dropout ( ) uses the same dropout mask throughout a sequence during training . DropConnect applies the dropout operation on the weight matrices . Zoneout ( ) , in a similar spirit with dropout , randomly chooses to use the previous time step hidden state instead of using the current one .
The first problem we tackle is low rank matrix learning which is a popular model assumption used in MTL . Trace norm regularization is a widely used approach for learning such models . A standard optimization strategy is based on formulating the problem as one of low rank matrix factorization which , however , leads to a non-convex problem .
As compared with the convex regularization approaches ( say , L1 regularization ) , however , the convergence issue of the corresponding algorithms are more difficult to tackle . In this paper , we deal with this difficult issue for a specific but typical nonconvex regularization scheme , the L1/2 regularization , which has been successfully used to many applications . More specifically , we study the convergence of the iterative half thresholding algorithm ( the half algorithm for short ) , one of the most efficient and important algorithms for solution to the L1/2 regularization .
Additionally , the well-known stochastic dual dynamic programming ( SDDP ) algorithm is customized and improved to drastically reduce the computation time and significantly improve solution quality when approximately solving this MSP model . Finally , and more generally , a novel nonconvex regularization scheme is developed to improve the computational performance of the SDDP algorithm for solving high-dimensional MSP models . Specifically , it is shown that these nonconvex regularization problems can be reformulated as mixed-integer programming problems with provable convergence guarantees .
It is widely hypothesized that image registration is an essential component in such systems . MethodsThe two non-rigid registration methods are : ( 1 ) a deformable registration of the prostate segmentation distance maps with B-spline regularization and ( 2 ) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data . We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures .
Here the newly developed method of determining the optimal regularization parameter α in uniform Tykhonov-Phillips regularization ( α-weighted S-homBLE ) by A-optimal design ( minimizing the trace of the Mean Square Error matrix MSE ) is reviewed . This new algorithm with A-optimal Regularization can be applied to overcome this kind problem in both GPS rapid static and real time kinematic positioning with single or dual frequency measurements , especially for the shorter period observation . In the case study , both the estimate methods are applied to process the two-epoch L1 data in single frequency GPS rapid static positioning .
The ℓ1 regularization problem has been widely used to solve the sparsity constrained problems . To enhance the sparsity constraint for better imaging performance , a promising direction is to use the ℓp norm ( 0 < ; p < ; 1 ) and solve the ℓp minimization problem .
In this paper , we propose a new $ \ell_{2,1}$-norm SVM , that is , multi-class hinge loss with a structured regularization term for all the classes to naturally select features for multi-class without bothering further heuristic strategy . Rather than directly solving the multi-class hinge loss with $ \ell_{2,1}$-norm regularization minimization , which has not been solved before due to its optimization difficulty , we are the first to give an efficient algorithm bridging the new problem with a previous solvable optimization problem to do multi-class feature selection . A global convergence proof for our method is also presented .
As such , most of the computational results claimed in the biophysics literature are difficult to reproduce . In this note , we give an exposition of this method , connect it with some related ideas in the literature , and propose a modification of the original method based on replacing mesh smoothing with harmonic energy regularization . We present a theoretical finding and related computational observations explaining why such a smoothing or regularization step is indispensable for the success of the algorithm .
Dynamic systems condition estimation regularization algorithms in the conditions of signals and hindrances statistical characteristics aprioristic uncertainty are offered . Regular iterative algorithms of strengthening matrix factor elements of the Kalman filter , allowing to adapt the filter to changing hindrance-alarm conditions are developed .
Therefore , in this project we want to investigate benefits of applying machine learning classification methodologies to Internet advertisement problem based on published images . In order to gain further insight into the nature of image-based classification problem in the context of Internet advertisement , we apply various regularization and sparsity promoting techniques to determine the features that are more dominant in making the distinguishment between the two classes of images .
Our model incorporates the Maximum Mean Discrepancy ( MMD ) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space . From experiments , we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set . We also show that our proposed model , preceded by the denoising auto-encoder pretraining , achieves better performance than recent benchmark models on the same data sets .
i s are parameters specific to two consecutive labels y i−1 and y i . For training , standard log - likelihood loss is used with L 2 regularization given a set of gold - standard instances . section : Experiments
p ( y = k|x ) of the model is most sensitive . [ reference ] and [ reference ] experimentally verified that the predictors trained with the standard regularization technique such as L 1 and L 2 regularization are likely to make mistakes when the signal is perturbed in the adversarial direction , even when the norm of the perturbation is so small that it can not be perceived by human eyes . Inspired by this finding , Goodfellow et al .
We show that adversarial training can provide an additional regularization benefit beyond that provided by using dropout dropout alone . Generic regularization strategies such as dropout , pretraining , and model averaging do not confer a significant reduction in a model ’s vulnerability to adversarial examples , but changing to nonlinear model families such as RBF networks can do so . Our explanation suggests a fundamental tension between designing models that are easy to train due to their linearity and designing models that use nonlinear effects to resist adversarial perturbation .
We therefore normalize through either projection or batch - normalization with fixed scale . We refer to the model minimizing ( [ reference ] ) with as Counterfactual Regression ( CFR ) and the variant without balance regularization ( ) as Treatment - Agnostic Representation Network ( TARNet ) . We train our models by minimizing ( [ reference ] ) using stochastic gradient descent , where we backpropagate the error through both the hypothesis and representation networks , as described in Algorithm [ reference ] .
We consider the problem of classifying nodes ( such as documents ) in a graph ( such as a citation network ) , where labels are only available for a small subset of nodes . This problem can be framed as graph - based semi - supervised learning , where label information is smoothed over the graph via some form of explicit graph - based regularization zhu2003semi , zhou2004learning , belkin2006manifold , weston2012deep , e.g. by using a graph Laplacian regularization term in the loss function : Here , denotes the supervised loss w.r.t . the labeled part of the graph , can be a neural network - like differentiable function , is a weighing factor and is a matrix of node feature vectors .
Revaud propose a method EpicFlow that uses externally matched flows as initialization and then performs interpolation . Zimmer design the complementary regularization that exploits directional information from the constraints imposed in data term . Our network that infers optical flow and performs flow regularization is inspired by the use of data fidelity and regularization in variational methods .
Through analysis and experiments , we elucidate connections between FractalNet and an array of phenomena engineered into previous deep network designs . As an additional contribution , we develop drop - path , a novel regularization protocol for ultra - deep fractal networks . Without data augmentation , fractal networks , trained with drop - path and dropout dropout , exceed the performance of residual networks regularized via stochastic depth huang2016stochasticdepth .
merity2016pointer based on perplexity . For fair comparison , we closely follow the regularization and optimization techniques introduced by merity2017regularizing . We heuristically and manually search hyper - parameters for MoS based on the validation performance while limiting the model size ( see Appendix [ reference ] for our hyper - parameters ) .
The behavior of the algorithms is similar to the one exhibited by the noisy artificial data above ( medium quality of graphs ) . For most observation levels our method combining nuclear norm and graph regularization ( red line ) clearly outperforms the rest . There are however two boundary phases that are noteworthy .
Here , regularization took several forms . First , in all layers , we bounded the absolute value of the kernel weights and applied both L1 and L2 regularization to prevent overfitting . This is done by adding the regularization terms to the negative log - probability ( i.e. , where and are coefficients for L1 and L2 regularization terms respectively ) .
Unlike the existing methods where dropout is applied only to the non-recurrent connections , the proposed method applies dropout to the recurrent connections as well in such a way that RNNs generalize well . Our experiments show that rnnDrop is a better regularization method than others including weight noise injection . Namely , when deep bidirectional long short-term memory ( LSTM ) RNNs were trained with rnnDrop as acoustic models for phoneme and speech recognition , they significantly outperformed the current state-of-the-arts ; we achieved the phoneme error rate of 16.29 % on the TIMIT core test set for phoneme recognition and the word error rate of 5.53 % on the Wall Street Journal ( WSJ ) dataset , dev93 , for speech recognition , which are the best reported results on both of the datasets .
We find that when appropriate forms of Dropout are applied in the right place in the model architecture that non-insignificant improvement in Mean Intersection over Union ( mIoU ) score can be observed . In our best case , we find that applying Dropout scheduling in conjunction with SpatialDropout improves baseline mIoU from 0.49 to 0.59 . This result shows that even where a model architecture makes extensive use of Batch Normalization , Dropout can still be an effective way of improving performance in low data situations .
Prediction of breast cancer recurrence using Classification Restricted Boltzmann Machine with Dropping Enhancement of Deep Architecture using Dropout/ DropConnect Techniques Applied for AHR System Spatially-Coupled Neural Network Architectures Stochastic Drop of Kernel Windows for Improved Generalization in Convolution Neural Networks Dropout and DropConnect for Reliable Neuromorphic Inference Under Communication Constraints in Network Connectivity DropConnect Regularization Method with Sparsity Constraint for Neural Networks Regularizing Neural Networks with Gradient Monitoring Dynamic Bayesian Neural Networks Fraternal Dropout Frameworks for Learning from Multiple Tasks $L_{1/2}$  Regularization: Convergence of Iterative Half Thresholding Algorithm Optimal Energy Storage Strategies in Microgrids Open-source image registration for MRI–TRUS fusion-guided prostate interventions The Uniform Tykhonov-Phillips Regularization (α-weighted S-homBLE) and its Application in GPS Rapid Static Positioning Sart-Type Half-Threshold Filtering Approach for CT Reconstruction Multi-Class L2,1-Norm Support Vector Machine Numerical Methods for Biomembranes Based on Piecewise Linear Surfaces STEADY ESTIMATION ALGORITHMS OF THE DYNAMIC SYSTEMS CONDITION ON THE BASIS OF CONCEPTS OF THE ADAPTIVE FILTRATION AND CONTROL Image-Based Internet Advertisement Classification Problem Domain Adaptive Neural Networks for Object Recognition Sentence-State LSTM for Text Representation Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Explaining and Harnessing Adversarial Examples Estimating individual treatment effect: generalization bounds and algorithms Semi-Supervised Classification with Graph Convolutional Networks LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation FractalNet: Ultra-Deep Neural Networks without Residuals Breaking the Softmax Bottleneck: A High-Rank RNN Language Model Matrix Completion on Graphs Brain tumor segmentation with Deep Neural Networks RNNDROP: A novel dropout for RNNS in ASR Don't ignore Dropout in Fully Convolutional Networks
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2013 2018 2019 2019 2019 2016 2019 2020 2018 2020 2013 2018 2015 2008 2014 2011 2017 2014 2014 2014 2018 2019 2015 2017 2017 2018 2017 2018 2014 2017 2015 2019
IJCNN IHSI INNSBDDL ICLR ICDM ACL IEEE Transactions on Pattern Analysis and Machine Intelligence ICLR ICML ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICLR ICLR NIPS 2014 Medical Image Anal. ASRU
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