Manifold Mixup is a modern method of data augmentation with a regularization effect that meld two images , or the feature maps corresponding to these images , and the targets are fused accordingly . We propose to apply the Manifold Mixup to text recognition while adapting it to work with a Connectionist Temporal Classification cost . We show that Manifold Mixup consistently improves text recognition results on various languages and datasets . In such a way we get the theoretical results for a broad range of H 1 –BV regularizations including the image denoising for the identity forward operator , the deconvolution problems for the convolution forward operator , or any other inverse problem for the continuous nonlinear forward operators . We demonstrate how the mixed regularization can be applied in practice . We suggest a denoising algorithm and compare its behavior with the BV regularization and the H 1 regularization . The improvement of imaging device 's sampling rate provides a clue to design a common approach for multi-images restoration . This paper concerns with a mixed-regularization approach for solving multi-images ( MRMI ) restoration problems . The MRMI algorithm generalizes the original total variation ( TV ) based algorithm by fusing multiple noisy images to maximize the useful information restored from the degraded images . Semi-supervised learning ( SSL ) has achieved attractive performance in many pattern recognition areas including image annotation , object recognition , face recognition and facial expression recognition . The state of the art SSL algorithm is Laplacian regularization ( LR ) which determined the underlying manifold using graph Laplacian . However , LR suffers from the lack of extrapolating power which will be towards the constant function for the data points beyond the boundary of domain . we show that the resulting algorithms inherit the convergence guarantees of ADMM . These methods are experimentally illustrated using frame-based regularization ; the results show the advantage of our approach over the use of the ―edge taper‖ function ( in terms of improvement in SNR ) . For the numerical solution of the model , we transform it into wavelet domain based on the wavelet characterization of dyadic Hardy space and dyadic BMO space , and solve it by the fixed iteration algorithm . Combing the total variation ( TV ) regularization method and frame based regularization method , a two-layers regularization model is proposed for edge and texture preserving , and then analyzed and solved in the frame of split Bregman method . Finally , we present various numerical results on images to demonstrate the potential of our methods . Tactile data have two salient properties : 1 ) sequentially real-time and 2 ) temporally correlated , which essentially calls for a real-time ( i.e. , online fixed-budget ) and recurrent recognition procedure . Based on an efficient and robust spatio-temporal feature representation for tactile sequences , we handle the problem of real-time recurrent tactile recognition by proposing a bounded online-sequential learning framework , and incorporates the strength of batch-regularization bootstrapping , bounded recursive reservoir , and momentum-based estimation . Experimental evaluations show that it outperforms the state-of-the-art methods by a large margin on test accuracy ; and its training performance is superior to most compared models from aspects of average online training error , computational complexity , and storage efficiency . However , using only few CSP features , though alleviates overfitting problem , loses the discriminating information , which limits the BCI performance . This letter proposes a novel feature weighting and regularization ( FWR ) method that utilizes all CSP features to avoid information loss . The proposed method can be applied in all CSP-based approaches . This article is devoted to the construction of a Hermite-type regularization operator transforming functions that are not necessarily C 1 into globally C 1 finite-element functions that are piecewise polynomials . This regularization operator is a projection , it preserves appropriate first and second order polynomial traces , and it has approximation properties of optimal order . In addition , we employ a joint training strategy to allow forward and backward decoding to improve each other in an interactive process . Experimental results show our proposed methods especially the second one ( bidirectional decoder regularization ) , leads a significantly improvement on both robustness and overall naturalness , as outperforming baseline ( the revised version of Tacotron2 ) with a MOS gap of 0.14 in a challenging test , and achieving close to human quality ( 4.42 vs. 4.49 in MOS ) on general test . Typically , such constraints are enforced by minimizing the L1 norm , which yields the so-called LASSO estimator . In this work , we propose a simple parameter transform that emulates sparse priors without sacrificing the simplicity and robustness of L2-norm regularization schemes . We show how L1 regularization can be obtained with a " sparsify " remapping of parameters under normal Bayesian priors , and we demonstrate the ensuing variational Laplace approach using Monte-Carlo simulations . We propose a novel SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function . The Lorentzian norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image , removing outliers in the data and Tikhonov regularization and Lorentzian Tikhonov regularization are used to remove artifacts from the final answer and improve the rate of convergence . The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for a several noise models such as noiseless , additive white Gaussian noise ( AWGN ) and salt & pepper noise where is the probability value that the correct label is assigned to in the sentence , is the L2 - norm regularization term , and is a hyperparameter . We call the second regularization term a successive regularization term . The successive regularization is based on the idea that we do not want the model to forget the information learned for the other tasks . There are several works that use a pre - trained discriminative model to aid the training of a generator . add a regularization term that encourages the reconstructed image to be similar to the original image in the feature space of a discriminative network . use an additional “ style loss ” based on Gram matrices of feature activations . Second , dropout can not be applied on the embedding before the contrastive loss , which introduces zero values at random locations . On the contrary , we can add dropout regularization on the embedding in the proposed model . Third , the DeepID networks are trained from scratch , while our model benefits from the networks pretrained on ImageNet . Performance of our single model is a little bit worse than performance of simultaneously published models . Compared to our work these models were trained with Dropout regularization which might improve single model performance . However , ensemble of our models outperforms these models even though they use pre - trained word embeddings . These lines are trained with PReLU as the activation function . These lines are trained with dropout regularization . subsection : Result from online A / B testing One possible solution to avoid this is to regularize the convolution filters . As a widely - used regularization method , orthogonal regularization has proven to be effective in eliminating the correlation between convolution filters , facilitating gradient propagation and improving the compactness of the learned model . In addition , recent studies have demonstrated the advantage of orthogonal regularization in enhancing the network generalization ability in applications of deep hashing and image classification . To this end , we train Generative Adversarial Networks at the largest scale yet attempted , and study the instabilities specific to such scale . We find that applying orthogonal regularization to the generator renders it amenable to a simple “ truncation trick , ” allowing fine control over the trade - off between sample fidelity and variety by reducing the variance of the Generator ’s input . Our modifications lead to models which set the new state of the art in class - conditional image synthesis . The relation with expectation - linear dropout has been discussed in Section [ reference ] . Here we perform experiments to study the difference in performance when using the ELD regularization versus our regularization ( FD ) . In addition to ELD , we also study a modification ( ELDM ) of ELD which applies target loss to both copies of LSTMs in ELD similar to FD ( notice in their case they only have dropout on one LSTM ) . Regularization An important extension to the stacked QRNN is a robust regularization scheme inspired by recent work in regularizing LSTMs . The need for an effective regularization method for LSTMs , and dropout ’s relative lack of efficacy when applied to recurrent connections , led to the development of recurrent dropout schemes , including variational inference Our main goal is to find a low - rank matrix that is structured by the proximities between users and movies . Introducing structure in sparse recovery problems is not new in the literature of compressed sensing , while similar structure inducing regularization has been proposed for factorized models for matrix completion . Yet , introducing structures via graphs in the convex low - rank matrix recovery setting is novel to the best of our knowledge . In addition , by stacking multiple Bidirectional LSTM layers to build the DBLSTM network , for the vibration signals measured under the complicated environment , more complex fault features can be effectively learned . With the increasement of the depth of the network , the regularization method using recurrent dropout technique is introduced to relieve the problem of overfitting and enhance the generalization ability of the deep network . The DBLSTM is assessed on the dataset of the wheelset bearings of the HSTs under different operating states . Recurrent neural networks ( RNNs ) serve as a fundamental building block for many sequence tasks across natural language processing . Recent research has focused on recurrent dropout techniques or custom RNN cells in order to improve performance . Both of these can require substantial modifications to the machine learning model or to the underlying RNN configurations . These recurrent layers were bidirectional RNNs , which were obtained by concatenating the forward hidden states ( collected by processing the sequence from the beginning to the end ) with backward hidden states ( gathered by scanning the speech in the reverse time order ) . Recurrent dropout was used as regularization technique . Since extending standard dropout to recurrent connections hinders learning long - term dependencies , we followed the approach introduced in , that tackles this issue by sharing the same dropout mask across all the time steps . However , a smaller performance gain is observed when batch normalization is applied to the CTC . This result could also be related to the different choice of the regularizer , as weight noise was used instead of recurrent dropout . In general , PERs are higher than those of the hybrid systems . It is one of the most commonly used benchmarks in language modelling . We consider two baseline models on PTB , a standard LSTM implementation with recurrent dropout , and the recent state - of - the - art AWD - LSTM . Our standard LSTM was taken from the Chainer tutorial on language modelling , and used two LSTM layers with 650 units each , trained with SGD and regularized with recurrent dropout . An important extension to the stacked QRNN is a robust regularization scheme inspired by recent work in regularizing LSTMs . The need for an effective regularization method for LSTMs , and dropout ’s relative lack of efficacy when applied to recurrent connections , led to the development of recurrent dropout schemes , including variational inference – based dropout Gal2015 and zoneout Krueger2016 . Stochastic Reward Nets ( SRN ) are used to model the system availability . We describe our SRN modeling using Stochastic Petri Net Package , and compute several interesting results that characterize HA-OSCAR availability . However , there is a distinct lack of the application of RNNs to real-world text classification tasks . This paper presents results on the capabilities of extended two-context layer SRN models ( xRNN ) applied to the classification of the Reuters-21578 corpus . The results show that the introduction of high levels of noise to sequences of words in titles , where noise is defined as the unimportant stopwords found in natural language text , is very robustly handled by the classifiers which maintain consistent levels of performance . The frequences are encoded in the word representations and define the words ' ' semantics . A simple Recurrent Network ( SRN ) parser combines the context frequences output . This disambiguation process is most striking when the interpretation involves semantic flipping , that is , an alternation between two opposing meanings as more words are read in . The sense of throwing a ball alternates between dance and baseball as indicators such as the agent , location , and recipient are input . The SRN parser demonstrates how the context frequencies are dynamically combined to determine the interpretation of such sentences . We hypothesize that other aspects of ambiguity resolution are based on similar mechanisms are well , and can be naturally approached from the distributed connectionist viewpoint . In this paper , we propose to apply a deep multi-scale encoder-decoder neural network ( MsEN-Net ) for SAR images enhancement method based on scale-recurrent network ( SRN ) , which consists of encoder and decoder modules trained by the coarse to fine strategy . Simulation with fixed speed errors and experiments with real data are implemented and evaluated by peak signal to noise ratio and structural similarity . Measured quantitatively , SRNs perform quite well . However , the way in which they achieve such performance diverges in key respects from the target grammar : ( i ) linear N-V-reflexive/pronoun sequences affect the SRN 's interpretations , even without a relevant structural relation , yielding errors unlike those made by humans ; ( ii ) the SRN 's representations distinguish sentence types , inhibiting structural generalization ; ( iii ) the SRN 's knowledge of the conditions on anaphoric dependencies fails to generalize to novel lexical items . These results have important consequences not only for the viability of SRNs as models of language learning but also for the systematicity of generalization in neural networks ( Hadley 1994 ; Marcus 1998 ) . Therefore , we present a unified object detection framework used for dense scenarios in retail images , which consists of the hierarchical labeling pattern , the Similarity Recognition Network ( SRN ) subnetwork and the optimized NMS algorithm on the basis of YOLOv3 . Three datasets are built to verify the effectiveness of the proposed framework , two of which are used for training the object detection network , and the other one is used for training the SRN subnetwork . The experimental results show that the proposed framework achieves a significant improvement for dense scenario in retail images compared with the traditional object detection algorithms . This paper presents a new routing protocol of network-on-chip(Noc ) called " Source Routing for Noc " ( SRN ) for fault tolerant communication of Systems-on-chip(Soc ) . The proposed SRN algorithm is composed of two mechanisms of route discovery and route maintenance to allow nodes to discover and maintain source routes to arbitrary destinations in Noc , and all aspects of the protocol operate entirely on-demand allowing the routing packet overhead to scale automatically based on its need . The SNR algorithm in this paper demonstrates up to 50 % more fault tolerance comparing with the conventional algorithms . The first , called the encoding SRN , receives a sequence of input letters and encodes them into a single distributed representation , called the bridge representation . The second , called the decoding SRN , decodes the bridge representation into a sequence of output letters . By conjoining these two SRNs , the sequence encoder can be trained to code all the letters of a word in order .
Manifold Mixup is a modern method of data augmentation with a regularization effect that meld two images , or the feature maps corresponding to these images , and the targets are fused accordingly . We propose to apply the Manifold Mixup to text recognition while adapting it to work with a Connectionist Temporal Classification cost . We show that Manifold Mixup consistently improves text recognition results on various languages and datasets .
In such a way we get the theoretical results for a broad range of H 1 –BV regularizations including the image denoising for the identity forward operator , the deconvolution problems for the convolution forward operator , or any other inverse problem for the continuous nonlinear forward operators . We demonstrate how the mixed regularization can be applied in practice . We suggest a denoising algorithm and compare its behavior with the BV regularization and the H 1 regularization .
The improvement of imaging device 's sampling rate provides a clue to design a common approach for multi-images restoration . This paper concerns with a mixed-regularization approach for solving multi-images ( MRMI ) restoration problems . The MRMI algorithm generalizes the original total variation ( TV ) based algorithm by fusing multiple noisy images to maximize the useful information restored from the degraded images .
Semi-supervised learning ( SSL ) has achieved attractive performance in many pattern recognition areas including image annotation , object recognition , face recognition and facial expression recognition . The state of the art SSL algorithm is Laplacian regularization ( LR ) which determined the underlying manifold using graph Laplacian . However , LR suffers from the lack of extrapolating power which will be towards the constant function for the data points beyond the boundary of domain .
we show that the resulting algorithms inherit the convergence guarantees of ADMM . These methods are experimentally illustrated using frame-based regularization ; the results show the advantage of our approach over the use of the ―edge taper‖ function ( in terms of improvement in SNR ) .
For the numerical solution of the model , we transform it into wavelet domain based on the wavelet characterization of dyadic Hardy space and dyadic BMO space , and solve it by the fixed iteration algorithm . Combing the total variation ( TV ) regularization method and frame based regularization method , a two-layers regularization model is proposed for edge and texture preserving , and then analyzed and solved in the frame of split Bregman method . Finally , we present various numerical results on images to demonstrate the potential of our methods .
Tactile data have two salient properties : 1 ) sequentially real-time and 2 ) temporally correlated , which essentially calls for a real-time ( i.e. , online fixed-budget ) and recurrent recognition procedure . Based on an efficient and robust spatio-temporal feature representation for tactile sequences , we handle the problem of real-time recurrent tactile recognition by proposing a bounded online-sequential learning framework , and incorporates the strength of batch-regularization bootstrapping , bounded recursive reservoir , and momentum-based estimation . Experimental evaluations show that it outperforms the state-of-the-art methods by a large margin on test accuracy ; and its training performance is superior to most compared models from aspects of average online training error , computational complexity , and storage efficiency .
However , using only few CSP features , though alleviates overfitting problem , loses the discriminating information , which limits the BCI performance . This letter proposes a novel feature weighting and regularization ( FWR ) method that utilizes all CSP features to avoid information loss . The proposed method can be applied in all CSP-based approaches .
This article is devoted to the construction of a Hermite-type regularization operator transforming functions that are not necessarily C 1 into globally C 1 finite-element functions that are piecewise polynomials . This regularization operator is a projection , it preserves appropriate first and second order polynomial traces , and it has approximation properties of optimal order .
In addition , we employ a joint training strategy to allow forward and backward decoding to improve each other in an interactive process . Experimental results show our proposed methods especially the second one ( bidirectional decoder regularization ) , leads a significantly improvement on both robustness and overall naturalness , as outperforming baseline ( the revised version of Tacotron2 ) with a MOS gap of 0.14 in a challenging test , and achieving close to human quality ( 4.42 vs. 4.49 in MOS ) on general test .
Typically , such constraints are enforced by minimizing the L1 norm , which yields the so-called LASSO estimator . In this work , we propose a simple parameter transform that emulates sparse priors without sacrificing the simplicity and robustness of L2-norm regularization schemes . We show how L1 regularization can be obtained with a " sparsify " remapping of parameters under normal Bayesian priors , and we demonstrate the ensuing variational Laplace approach using Monte-Carlo simulations .
We propose a novel SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function . The Lorentzian norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image , removing outliers in the data and Tikhonov regularization and Lorentzian Tikhonov regularization are used to remove artifacts from the final answer and improve the rate of convergence . The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for a several noise models such as noiseless , additive white Gaussian noise ( AWGN ) and salt & pepper noise
where is the probability value that the correct label is assigned to in the sentence , is the L2 - norm regularization term , and is a hyperparameter . We call the second regularization term a successive regularization term . The successive regularization is based on the idea that we do not want the model to forget the information learned for the other tasks .
There are several works that use a pre - trained discriminative model to aid the training of a generator . add a regularization term that encourages the reconstructed image to be similar to the original image in the feature space of a discriminative network . use an additional “ style loss ” based on Gram matrices of feature activations .
Second , dropout can not be applied on the embedding before the contrastive loss , which introduces zero values at random locations . On the contrary , we can add dropout regularization on the embedding in the proposed model . Third , the DeepID networks are trained from scratch , while our model benefits from the networks pretrained on ImageNet .
Performance of our single model is a little bit worse than performance of simultaneously published models . Compared to our work these models were trained with Dropout regularization which might improve single model performance . However , ensemble of our models outperforms these models even though they use pre - trained word embeddings .
These lines are trained with PReLU as the activation function . These lines are trained with dropout regularization . subsection : Result from online A / B testing
One possible solution to avoid this is to regularize the convolution filters . As a widely - used regularization method , orthogonal regularization has proven to be effective in eliminating the correlation between convolution filters , facilitating gradient propagation and improving the compactness of the learned model . In addition , recent studies have demonstrated the advantage of orthogonal regularization in enhancing the network generalization ability in applications of deep hashing and image classification .
To this end , we train Generative Adversarial Networks at the largest scale yet attempted , and study the instabilities specific to such scale . We find that applying orthogonal regularization to the generator renders it amenable to a simple “ truncation trick , ” allowing fine control over the trade - off between sample fidelity and variety by reducing the variance of the Generator ’s input . Our modifications lead to models which set the new state of the art in class - conditional image synthesis .
The relation with expectation - linear dropout has been discussed in Section [ reference ] . Here we perform experiments to study the difference in performance when using the ELD regularization versus our regularization ( FD ) . In addition to ELD , we also study a modification ( ELDM ) of ELD which applies target loss to both copies of LSTMs in ELD similar to FD ( notice in their case they only have dropout on one LSTM ) .
Regularization An important extension to the stacked QRNN is a robust regularization scheme inspired by recent work in regularizing LSTMs . The need for an effective regularization method for LSTMs , and dropout ’s relative lack of efficacy when applied to recurrent connections , led to the development of recurrent dropout schemes , including variational inference
Our main goal is to find a low - rank matrix that is structured by the proximities between users and movies . Introducing structure in sparse recovery problems is not new in the literature of compressed sensing , while similar structure inducing regularization has been proposed for factorized models for matrix completion . Yet , introducing structures via graphs in the convex low - rank matrix recovery setting is novel to the best of our knowledge .
In addition , by stacking multiple Bidirectional LSTM layers to build the DBLSTM network , for the vibration signals measured under the complicated environment , more complex fault features can be effectively learned . With the increasement of the depth of the network , the regularization method using recurrent dropout technique is introduced to relieve the problem of overfitting and enhance the generalization ability of the deep network . The DBLSTM is assessed on the dataset of the wheelset bearings of the HSTs under different operating states .
Recurrent neural networks ( RNNs ) serve as a fundamental building block for many sequence tasks across natural language processing . Recent research has focused on recurrent dropout techniques or custom RNN cells in order to improve performance . Both of these can require substantial modifications to the machine learning model or to the underlying RNN configurations .
These recurrent layers were bidirectional RNNs , which were obtained by concatenating the forward hidden states ( collected by processing the sequence from the beginning to the end ) with backward hidden states ( gathered by scanning the speech in the reverse time order ) . Recurrent dropout was used as regularization technique . Since extending standard dropout to recurrent connections hinders learning long - term dependencies , we followed the approach introduced in , that tackles this issue by sharing the same dropout mask across all the time steps .
However , a smaller performance gain is observed when batch normalization is applied to the CTC . This result could also be related to the different choice of the regularizer , as weight noise was used instead of recurrent dropout . In general , PERs are higher than those of the hybrid systems .
It is one of the most commonly used benchmarks in language modelling . We consider two baseline models on PTB , a standard LSTM implementation with recurrent dropout , and the recent state - of - the - art AWD - LSTM . Our standard LSTM was taken from the Chainer tutorial on language modelling , and used two LSTM layers with 650 units each , trained with SGD and regularized with recurrent dropout .
An important extension to the stacked QRNN is a robust regularization scheme inspired by recent work in regularizing LSTMs . The need for an effective regularization method for LSTMs , and dropout ’s relative lack of efficacy when applied to recurrent connections , led to the development of recurrent dropout schemes , including variational inference – based dropout Gal2015 and zoneout Krueger2016 .
Stochastic Reward Nets ( SRN ) are used to model the system availability . We describe our SRN modeling using Stochastic Petri Net Package , and compute several interesting results that characterize HA-OSCAR availability .
However , there is a distinct lack of the application of RNNs to real-world text classification tasks . This paper presents results on the capabilities of extended two-context layer SRN models ( xRNN ) applied to the classification of the Reuters-21578 corpus . The results show that the introduction of high levels of noise to sequences of words in titles , where noise is defined as the unimportant stopwords found in natural language text , is very robustly handled by the classifiers which maintain consistent levels of performance .
The frequences are encoded in the word representations and define the words ' ' semantics . A simple Recurrent Network ( SRN ) parser combines the context frequences output . This disambiguation process is most striking when the interpretation involves semantic flipping , that is , an alternation between two opposing meanings as more words are read in . The sense of throwing a ball alternates between dance and baseball as indicators such as the agent , location , and recipient are input . The SRN parser demonstrates how the context frequencies are dynamically combined to determine the interpretation of such sentences . We hypothesize that other aspects of ambiguity resolution are based on similar mechanisms are well , and can be naturally approached from the distributed connectionist viewpoint .
In this paper , we propose to apply a deep multi-scale encoder-decoder neural network ( MsEN-Net ) for SAR images enhancement method based on scale-recurrent network ( SRN ) , which consists of encoder and decoder modules trained by the coarse to fine strategy . Simulation with fixed speed errors and experiments with real data are implemented and evaluated by peak signal to noise ratio and structural similarity .
Measured quantitatively , SRNs perform quite well . However , the way in which they achieve such performance diverges in key respects from the target grammar : ( i ) linear N-V-reflexive/pronoun sequences affect the SRN 's interpretations , even without a relevant structural relation , yielding errors unlike those made by humans ; ( ii ) the SRN 's representations distinguish sentence types , inhibiting structural generalization ; ( iii ) the SRN 's knowledge of the conditions on anaphoric dependencies fails to generalize to novel lexical items . These results have important consequences not only for the viability of SRNs as models of language learning but also for the systematicity of generalization in neural networks ( Hadley 1994 ; Marcus 1998 ) .
Therefore , we present a unified object detection framework used for dense scenarios in retail images , which consists of the hierarchical labeling pattern , the Similarity Recognition Network ( SRN ) subnetwork and the optimized NMS algorithm on the basis of YOLOv3 . Three datasets are built to verify the effectiveness of the proposed framework , two of which are used for training the object detection network , and the other one is used for training the SRN subnetwork . The experimental results show that the proposed framework achieves a significant improvement for dense scenario in retail images compared with the traditional object detection algorithms .
This paper presents a new routing protocol of network-on-chip(Noc ) called " Source Routing for Noc " ( SRN ) for fault tolerant communication of Systems-on-chip(Soc ) . The proposed SRN algorithm is composed of two mechanisms of route discovery and route maintenance to allow nodes to discover and maintain source routes to arbitrary destinations in Noc , and all aspects of the protocol operate entirely on-demand allowing the routing packet overhead to scale automatically based on its need . The SNR algorithm in this paper demonstrates up to 50 % more fault tolerance comparing with the conventional algorithms .
The first , called the encoding SRN , receives a sequence of input letters and encodes them into a single distributed representation , called the bridge representation . The second , called the decoding SRN , decodes the bridge representation into a sequence of output letters . By conjoining these two SRNs , the sequence encoder can be trained to code all the letters of a word in order .
Manifold Mixup Improves Text Recognition with CTC Loss Mixed Tikhonov regularization in Banach spaces based on domain decomposition Multi-Images Restoration Method with a Mixed-Regularization Approach for Cognitive Informatics Facial expression recognition based on Hessian regularized support vector machine Performance of Alternating Direction Methodof Multipliers(ADMM) Based on DeconvolvingImages with Unknown Boundaries Image Restoration Models Based on Dyadic Hardy Space and Dyadic Bounded Mean Oscillation Space Real-Time Recurrent Tactile Recognition: Momentum Batch-Sequential Echo State Networks Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI Hermite interpolation of nonsmooth functions preserving boundary conditions Forward-Backward Decoding for Regularizing End-to-End TTS On parameters transformations for emulating sparse priors using variational-Laplace inference A Lorentzian Bayesian Approach for Robust Iterative Multiframe Super-Resolution Reconstruction with Lorentzian-Tikhonov Regularization A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks Stacked Generative Adversarial Networks A Discriminatively Learned CNN Embedding for Person Reidentification Text Understanding with the Attention Sum Reader Network Deep Interest Network for Click-Through Rate Prediction FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising Large Scale GAN Training for High Fidelity Natural Image Synthesis Fraternal Dropout Quasi-Recurrent Neural Networks Matrix Completion on Graphs Fault Diagnosis of Wheelset Bearings using Deep Bidirectional Long Short-term Memory Network Revisiting Activation Regularization for Language RNNs Light Gated Recurrent Units for Speech Recognition Dynamic Evaluation of Neural Sequence Models Quasi-Recurrent Neural Networks Achieving high availability and performance computing with an HA-OSCAR cluster Robust Text Classification Using a Hysteresis-Driven Extended SRN Lexical Disambiguation Based on Distributed Representations of ContextFrequency SAR Images Enhancement Via Deep Multi-Scale Encoder-Decoder Neural Network The Acquisition of Anaphora by Simple Recurrent Networks A Unified Object Detection Framework for Intelligent Retail Container Commodities Fault Tolerant Source Routing for Network-on-chip Learning Representations of Orthographic Word Forms
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ICCI*CC ICIMCS EMNLP 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ACM Trans. Multim. Comput. Commun. Appl. ACL KDD IEEE Transactions on Image Processing ICLR ICLR ICLR NIPS 2014 IEEE Transactions on Emerging Topics in Computational Intelligence ICML ICLR ICANN (2) IGARSS ICIP DFT
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