In order to examine the impacts of those non-ideal effects , we first develop a comprehensive framework called PytorX based on main-stream DNN pytorch framework . PytorX could perform end-to-end training , mapping , and evaluation for crossbar-based neural network accelerator , considering all above discussed non-ideal effects of ReRAM crossbar together . Experiments based on PytorX show that directly mapping the trained large scale DNN into crossbar without considering these non-ideal effects could lead to a complete system malfunction ( i.e. , equal to random guess ) when the neural network goes deeper and wider . These problems can be addressed by sparsification of an over-complete model , which removes redundant parameters or connections by pruning them away after training or encouraging them to become zero during training . In general , however , these approaches are not fully differentiable and interrupt an end-to-end training process with the stochastic gradient descent in that they require either a parameter selection or a soft-thresholding step . In this paper , we propose a fully differentiable sparsification method for deep neural networks , which allows parameters to be exactly zero during training , and thus can learn the sparsified structure and the weights of networks simultaneously using the stochastic gradient descent . While both networks perform similarly overall , recognition accuracy increases progressively with each added layer , with discriminative features learned in every stage of the network , whereas in end – end training , no such systematic feature representation was observed . We also show that such cascade training has significant computational and memory advantages over end – end training , and can be used as a pretraining algorithm to obtain a better performance . Convolutional Neural Networks ( CNNs ) based algorithms have been successful in solving image recognition problems , showing very large accuracy improvement . In recent years , deconvolution layers are widely used as key components in the state-of-the-art CNNs for end-to-end training and models to support tasks such as image segmentation . However , the deconvolution algorithms are computationally intensive which limits their applicability to real time applications . Then we perform a two-stream feature fusion operation to aggregate the contextual information in both the global image and lung field for complementary feature learning . By taking advantage of the two-stream deep structures and two types of image inputs , a novel self-adaptive weighted fusion scheme is designed to jointly learn these two feature streams in the end-to-end training phase , in order to realize adaptive two-stream feature subsets selection and optimization . Extensive experiments on the ChestX-ray14 dataset demonstrate the effectiveness of the proposed method as compared with the state-of-the-art baselines . Our purpose in this study is to investigate whether a recently introduced image transform , denoted as the Radon cumulative distribution transform ( R-CDT ) , can be used as a viable preprocessing step for augmenting the robustness of training end-to-end systems with fewer training samples . In order to assess the ability of the R-CDT to perform this aim , we identified a standard machine learning dataset , MNIST , and a preliminary dataset comprised of liver cell nuclei images derived from one of two tissue types : benign or malignant tumor lesions . To improve performance on natural images , we incorporate a lightweight autoregressive model in the reconstruction distribution . These techniques permit end-to-end training of models with 10 + layers of latent variables . Experiments show that our approach achieves state-of-the-art performance on standard image modelling benchmarks , can expose latent class structure in the absence of label information , and can provide convincing imputations of occluded regions in natural images . Specifically , three NN components are constructed to learn the behavior of the transmitter , relay node , and receiver , respectively . As the conventional end-to-end training is inapplicable , a novel two-stage training approach is proposed to indirectly solve the end-to-end training problem . The implicit approximations involved are analytically expressed based on information theory , offering insights on the achievable performance with the proposed training method . Firstly , observing the limited rotation-invariance performance of DNN , we proposed the Multi-Angle Feature Aggregation ( MAFA ) method , leveraging active image rotation to gain richer visual cues and make the prediction more robust to instrument orientation changes . Secondly , in the end-to-end training stage , the auxiliary contour supervision is utilized to guide the model to learn the boundary awareness , so that the contour shape of segmentation mask is more precise . The proposed method is validated with ablation experiments on the novel Sinus-Surgery datasets collected from surgeons ’ operations , and is compared to the existing methods on a public dataset collected with a da Vinci Xi Robot . The main goal of end-to-end training is the model being optimized to be consistent with the speaker verification protocol . The end- to-end training jointly learns the background and speaker models by creating the representation space . The LSTM architecture is trained to create a discrimination space for validating the match and non-match pairs for speaker verification . From the figure , we can see the mean shift grouping transforms the initial embedding vectors to yield a small set of instance labels which are distinct ( for negative pairs ) and compact ( for positive pairs ) . subsection : End - to - end training It ’s straightforward to compute the derivatives of the recurrent mean shift grouping module w.r.t based on the the chain rule so our whole system is end - to - end trainable through back - propagation . In this section we describe the definition for each stage . In the next section we introduce an end - to - end training algorithm to address the causal dependency . subsection : Regressing Box - level Instances However , this is not the case in the presence of . Gradients of both terms need to be considered in a theoretically sound end - to - end training solution . In this section , we develop a differentiable RoI warping layer to account for the gradient predicted box positions and address the dependency on . The features are naturally shared by optimizing a unified loss function , and the benefits of multi - task learning are witnessed . Table [ reference ] ( d ) shows the result of end - to - end training a 5 - stage cascade . The mAP is further improved to 63.5 % . The proposed quality aware network automatically generates quality scores for each image in a set and leads to better representation for set - to - set recognition . We design an end - to - end training strategy and demonstrate that the quality generation part and feature generation part benefit from each other during back propagation . Quality learnt by QAN is better than quality estimated by human and we achieves new state - of - the - art performance on four benchmarks for person re - identification and face verification . Though it may be suited well for arbitrary shape of text in natural images , it often involves several pipelines which leads to inefficiency . Inspired by recent advance on instance - aware semantic segmentation , we present an end - end trainable framework called Fused Text Segmentation Networks ( FTSN ) to handle arbitrary - shape text detection with no extra pipelines involved . It inherits merits from both object detection and semantic segmentation architecture which efficiently detects and segments an text instance simultaneously and accurately gives predictions in the pixel level . Moreover , our results on semantic part segmentation ( which is not - instance aware ) is also competitive with current state - of - theart . All of these results are achieved with a holistic , end - to - end trained model which parses humans at both an instance and category level , and outputs a dynamic number of instances per image , all in a single forward - pass through the network . section : Related Work From the point of view of , the gradient of Block 1 is , where the first term is non - zero . will thus update Block 1 at each iteration . Considering the non - linearity in Block 3 , it is non - trivial to tell if this is as good as the fully end - to - end training case , as illustrated by in the top right example . An investigation of this issue remains future work . We have analysed the ResNet architecture , in terms of the ensemble classifiers therein and the effective depths of the residual units . On the basis of that analysis we calculated a new , more spatially efficient , and better performing architecture which actually achieves fully end - to - end training for large networks . Using this new architecture we designed a group of correspondingly shallow networks , and showed that they outperform the previous very deep residual networks not only on the ImageNet classification dataset , but also when applied to semantic image segmentation . The success of our models is primarily attributed to extensive parameter sharing between the encoder and decoder parts of the architecture , coupled with the capability to learn expressive non - linear latent node representations from both local graph neighborhoods and explicit node features . Further , our novel architecture is capable of simultaneous multi - task learning of both link prediction and node classification in one efficient end - to - end training stage . Our work provides a useful framework to make accurate and meaningful predictions on a diverse set of complex graph structures for a wide range of real - world applications . Instead we predict multiple camera pose hypotheses as well as the respective uncertainty for each prediction . Towards this aim , we use Bingham distributions , to model the orientation of the camera pose , and a multivariate Gaussian to model the position , with an end-to-end deep neural network . By incorporating a Winner-Takes-All training scheme , we finally obtain a mixture model that is well suited for explaining ambiguities in the scene , yet does not suffer from mode collapse , a common problem with mixture density networks . We define this approach as variational synthesis of mechanisms . In addition , we also present an alternate end-to-end deep neural network architecture for variational synthesis of linkages . This end-to-end architecture is a conditional-VAE , which approximates the conditional distribution of linkage parameters with respect to a coupler trajectory distribution . Unlike the existing techniques , we do not use any manually crafted feature , instead directly operate on Lagrangian fluid simulation data and use machine learned features . This paper presents a novel end-to-end deep learning neural network that can automatically generate a model for fluid animation based-on Lagrangian fluid simulation data . This approach synthesizes velocity fields with irregular Lagrangian data structure using neural network . However , we argue that multi-step citywide passenger demands encapsulate both time-varying demand trends and global statuses , and hence are more beneficial to avoiding demand-service mismatching and developing effective vehicle distribution/scheduling strategies . In this paper , we propose an end-to-end deep neural network solution to the prediction task . We employ the encoder-decoder framework based on convolutional and ConvLSTM units to identify complex features that capture spatiotemporal influences and pickup-dropoff interactions on citywide passenger demands . We develop a data driven approach and learn both the measurement matrix and the inverse reconstruction scheme instead of utilizing random linear projections as measurements and reconstruction via convex optimization with a given known sparsity in conventional CS framework . To achieve this goal , we have designed an end to end deep neural network structure consisting of fully connected layers with cascaded convolutional layers to be trained and tested over a publicly available image dataset . Results show that the proposed technique provides higher peak signal to noise ratio ( PSNR ) levels and hence learns better measurement matrices than that of the randomly selected or specifically designed for a known sparsity basis to reduce average coherence . However , networks designed for these tasks typically require vast quantities of training data and long training periods to converge . We investigate the design rationale behind end-to-end driving network designs by proposing and comparing three small and computationally inexpensive deep end-to-end neural network models that generate driving control signals directly from input images . In contrast to prior work that segments the autonomous driving task , our models take on a novel approach to the autonomous driving problem by utilizing deep and thin Fully Convolutional Nets ( FCNs ) with recurrent neural nets and low parameter counts to tackle a complex end-to-end regression task predicting both steering and acceleration commands . In this paper , we examine three types of uncertainty : model capacity uncertainty , intrinsic data uncertainty , and open set uncertainty , and review techniques that have been derived to address each one . We then introduce a unified hierarchical model , which combines methods from Bayesian inference , invertible latent density inference , and discriminative classification in a single end-to-end deep neural network topology to yield efficient per-sample uncertainty estimation . Our approach addresses all three uncertainty types and readily accommodates prior/base rates for binary detection . Each code is composed of multiple discrete numbers , such as , where the value of each component is limited to a fixed range . We propose to directly learn the discrete codes in an end - to - end neural network by applying the Gumbel - softmax trick . Experiments show the compression rate achieves in a sentiment analysis task and in machine translation tasks without performance loss . The authors propose an approach based on handcrafted features using a semantic parse of the question and scene analysis of the image combined in a latent - world Bayesian framework . More recently , several end - to - end deep neural networks that learn features directly from data have been applied to this problem . Most of these are directly adapted from captioning models , and utilize a recurrent LSTM network , which takes the question and Convolutional Neural Net ( CNN ) image features as input , and outputs the answer . subsection : QAN for image set embedding In this paper , feature generation and aggregation module is implemented through an end - to - end convolutional neural network named QAN as shown in Fig . [ reference ] . Weakly supervised object detection only requires supervision at image category level . Bilen and Vedaldi presents an end - to - end deep network for WSOD , in which final image classification score is the weighted sum of proposal scores , that is , each proposal contributes a percentage to the final image classification . The deep network can correctly classify image even only “ see ” a part of object , and as a result , the top ranking proposal may fail to meet the standard object detection requirement ( IoU 0.5 between ground truths and predicted boxes ) . In this study , we propose a style - controllable Multi - source Abstractive Summarization model for QUEstion answering , called Masque . The model is an end - to - end deep neural network that can generate answers conditioned on a given style . Experiments with MS MARCO 2.1 show that our model achieved state - of - the - art performance on two tasks with different answer styles . The contributions of this paper are mainly in three aspects . First , we propose a novel end - to - end neural network architecture that generates a 3D mesh model from a single RGB image . Second , we design a projection layer which incorporates perceptual image features into the 3D geometry represented by GCN . Feature - based models represent a premise and a hypothesis by their unlexicalized and lexicalized features , such as - gram length and the real - valued feature of length difference , then train a classifier to perform relation classification . Recently , end - to - end neural network - based models have drawn worldwide attention since they have demonstrated excellent performance on quite a few NLP tasks including machine translation , natural language inference , etc . On the basis of their model structures , we can divide neural network - based models for NLI into two classes , sentence encoding models and sentence interaction - aggregation models . While prior work has addressed intra - class variation using localization and segmentation techniques , inter - class similarity may also affect feature learning and reduce classification performance . In this work , we address this problem using a novel optimization procedure for the end - to - end neural network training on FGVC tasks . Our procedure , called Pairwise Confusion ( PC ) reduces overfitting by intentionally introducing confusion in the activations . Their loss function is permutation - invariant as it incorporates the Hungarian algorithm to match candidate hypotheses to ground - truth instances . Inspired by their work , Romera propose an end - to - end recurrent network with convolutional LSTMs that sequentially outputs binary segmentation maps for each instance . Ren improve upon by adding a box network to confine segmentations within a local window and skip connections instead of graphical models to restore the resolution at the output . In particular , the proposed CRF - RNN can be plugged in as a part of a traditional deep neural network : It is capable of passing on error differentials from its outputs to inputs during back - propagation based training of the deep network while learning CRF parameters . We demonstrate the use of this approach by utilizing it for the semantic segmentation task : we form an end - to - end trainable deep network by combining a fully convolutional neural network with the CRF - RNN . Our system achieves a new state - of - the - art on the popular Pascal VOC segmentation benchmark . They simply suppose : ( 1 ) a hard rule among user behaviors ( e.g. , add-to-cart must come before purchase and after view ) ; ( 2 ) merge repeated behaviors into one ( e.g. , view several times is considered as view once only ) , thus failing to unveil user preferences from their real behaviors . To ease these issues , we , therefore , propose a novel end-to-end neural framework – TDRB , which automatically models the Temporal Dynamics and Repeated Behaviors to assist in capturing user preference , thus achieving more accurate recommendations . Empirical studies on three real-world datasets demonstrate the superiority of our proposed TDRB against other state-of-the-arts . Conventional decompilers have the following major limitations : ( i ) they are only applicable to specific source-target language pair , hence incurs undesired development cost for new language tasks ; ( ii ) their output high-level code can not effectively preserve the correct functionality of the input binary ; ( iii ) their output program does not capture the semantics of the input and the reversed program is hard to interpret . To address the above problems , we propose Coda , the first end-to-end neural-based framework for code decompilation . Coda decomposes the decompilation task into two key phases : First , Coda employs an instruction type-aware encoder and a tree decoder for generating an abstract syntax tree ( AST ) with attention feeding during the code sketch generation stage . In dialogue systems , the task of dialogue generation aims to generate utterances given previous utterances as contexts . Among various spectrums of dialogue generation approaches , end-to-end neural generation models have received an increase of attention . These end-to-end neural generation models are capable of generating natural-sounding sentences with a unified neural encoder-decoder network structure . In recent years , all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition ( ASR ) tasks . However , most existing works focus on building ASR models where train and test data are drawn from the same domain . Speech enhancement systems , which denoise and dereverberate distorted signals , are usually optimized based on signal reconstruction objectives including the maximum likelihood and minimum mean square error . However , emergent end-to-end neural methods enable to optimize the speech enhancement system with more application-oriented objectives . For example , we can jointly optimize speech enhancement and automatic speech recognition ( ASR ) only with ASR error minimization criteria . Although more recent neural network-based methods attempt to integrate target information using attention mechanism , the performance improvement is rather limited due to the underuse of this information . To address this issue , we propose an endto- end neural model , TGMN-CR , which makes better use of target content information . Specifically , our model first learns conditional tweet representation with respect to specific target . These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without deepening into the rationality and superiority of neural network design . In this paper , we delve into an effective end-to-end neural network structure for stronger feature expression and spatial correlation learning . Specifically , we propose a non-locally enhanced encoder-decoder network framework , which consists of a pooling indices embedded encoder-decoder network to efficiently learn increasingly abstract feature representation for more accurate rain streaks modeling while perfectly preserving the image detail . We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers . We first investigate how end-to-end neural sequence models ( with pre-trained language model representations ) perform on document-level role filler extraction , as well as how the length of context captured affects the models ' performance . To dynamically aggregate information captured by neural representations learned at different levels of granularity ( e.g. , the sentence- and paragraph-level ) , we propose a novel multi-granularity reader . Compared with MRC on a single passage , multi - passage MRC is more challenging , since we are likely to get multiple confusing answer candidates from different passages . To address this problem , we propose an end - to - end neural model that enables those answer candidates from different passages to verify each other based on their content representations . Specifically , we jointly train three modules that can predict the final answer based on three factors : the answer boundary , the answer content and the cross - passage answer verification .
In order to examine the impacts of those non-ideal effects , we first develop a comprehensive framework called PytorX based on main-stream DNN pytorch framework . PytorX could perform end-to-end training , mapping , and evaluation for crossbar-based neural network accelerator , considering all above discussed non-ideal effects of ReRAM crossbar together . Experiments based on PytorX show that directly mapping the trained large scale DNN into crossbar without considering these non-ideal effects could lead to a complete system malfunction ( i.e. , equal to random guess ) when the neural network goes deeper and wider .
These problems can be addressed by sparsification of an over-complete model , which removes redundant parameters or connections by pruning them away after training or encouraging them to become zero during training . In general , however , these approaches are not fully differentiable and interrupt an end-to-end training process with the stochastic gradient descent in that they require either a parameter selection or a soft-thresholding step . In this paper , we propose a fully differentiable sparsification method for deep neural networks , which allows parameters to be exactly zero during training , and thus can learn the sparsified structure and the weights of networks simultaneously using the stochastic gradient descent .
While both networks perform similarly overall , recognition accuracy increases progressively with each added layer , with discriminative features learned in every stage of the network , whereas in end – end training , no such systematic feature representation was observed . We also show that such cascade training has significant computational and memory advantages over end – end training , and can be used as a pretraining algorithm to obtain a better performance .
Convolutional Neural Networks ( CNNs ) based algorithms have been successful in solving image recognition problems , showing very large accuracy improvement . In recent years , deconvolution layers are widely used as key components in the state-of-the-art CNNs for end-to-end training and models to support tasks such as image segmentation . However , the deconvolution algorithms are computationally intensive which limits their applicability to real time applications .
Then we perform a two-stream feature fusion operation to aggregate the contextual information in both the global image and lung field for complementary feature learning . By taking advantage of the two-stream deep structures and two types of image inputs , a novel self-adaptive weighted fusion scheme is designed to jointly learn these two feature streams in the end-to-end training phase , in order to realize adaptive two-stream feature subsets selection and optimization . Extensive experiments on the ChestX-ray14 dataset demonstrate the effectiveness of the proposed method as compared with the state-of-the-art baselines .
Our purpose in this study is to investigate whether a recently introduced image transform , denoted as the Radon cumulative distribution transform ( R-CDT ) , can be used as a viable preprocessing step for augmenting the robustness of training end-to-end systems with fewer training samples . In order to assess the ability of the R-CDT to perform this aim , we identified a standard machine learning dataset , MNIST , and a preliminary dataset comprised of liver cell nuclei images derived from one of two tissue types : benign or malignant tumor lesions .
To improve performance on natural images , we incorporate a lightweight autoregressive model in the reconstruction distribution . These techniques permit end-to-end training of models with 10 + layers of latent variables . Experiments show that our approach achieves state-of-the-art performance on standard image modelling benchmarks , can expose latent class structure in the absence of label information , and can provide convincing imputations of occluded regions in natural images .
Specifically , three NN components are constructed to learn the behavior of the transmitter , relay node , and receiver , respectively . As the conventional end-to-end training is inapplicable , a novel two-stage training approach is proposed to indirectly solve the end-to-end training problem . The implicit approximations involved are analytically expressed based on information theory , offering insights on the achievable performance with the proposed training method .
Firstly , observing the limited rotation-invariance performance of DNN , we proposed the Multi-Angle Feature Aggregation ( MAFA ) method , leveraging active image rotation to gain richer visual cues and make the prediction more robust to instrument orientation changes . Secondly , in the end-to-end training stage , the auxiliary contour supervision is utilized to guide the model to learn the boundary awareness , so that the contour shape of segmentation mask is more precise . The proposed method is validated with ablation experiments on the novel Sinus-Surgery datasets collected from surgeons ’ operations , and is compared to the existing methods on a public dataset collected with a da Vinci Xi Robot .
The main goal of end-to-end training is the model being optimized to be consistent with the speaker verification protocol . The end- to-end training jointly learns the background and speaker models by creating the representation space . The LSTM architecture is trained to create a discrimination space for validating the match and non-match pairs for speaker verification .
From the figure , we can see the mean shift grouping transforms the initial embedding vectors to yield a small set of instance labels which are distinct ( for negative pairs ) and compact ( for positive pairs ) . subsection : End - to - end training It ’s straightforward to compute the derivatives of the recurrent mean shift grouping module w.r.t based on the the chain rule so our whole system is end - to - end trainable through back - propagation .
In this section we describe the definition for each stage . In the next section we introduce an end - to - end training algorithm to address the causal dependency . subsection : Regressing Box - level Instances
However , this is not the case in the presence of . Gradients of both terms need to be considered in a theoretically sound end - to - end training solution . In this section , we develop a differentiable RoI warping layer to account for the gradient predicted box positions and address the dependency on .
The features are naturally shared by optimizing a unified loss function , and the benefits of multi - task learning are witnessed . Table [ reference ] ( d ) shows the result of end - to - end training a 5 - stage cascade . The mAP is further improved to 63.5 % .
The proposed quality aware network automatically generates quality scores for each image in a set and leads to better representation for set - to - set recognition . We design an end - to - end training strategy and demonstrate that the quality generation part and feature generation part benefit from each other during back propagation . Quality learnt by QAN is better than quality estimated by human and we achieves new state - of - the - art performance on four benchmarks for person re - identification and face verification .
Though it may be suited well for arbitrary shape of text in natural images , it often involves several pipelines which leads to inefficiency . Inspired by recent advance on instance - aware semantic segmentation , we present an end - end trainable framework called Fused Text Segmentation Networks ( FTSN ) to handle arbitrary - shape text detection with no extra pipelines involved . It inherits merits from both object detection and semantic segmentation architecture which efficiently detects and segments an text instance simultaneously and accurately gives predictions in the pixel level .
Moreover , our results on semantic part segmentation ( which is not - instance aware ) is also competitive with current state - of - theart . All of these results are achieved with a holistic , end - to - end trained model which parses humans at both an instance and category level , and outputs a dynamic number of instances per image , all in a single forward - pass through the network . section : Related Work
From the point of view of , the gradient of Block 1 is , where the first term is non - zero . will thus update Block 1 at each iteration . Considering the non - linearity in Block 3 , it is non - trivial to tell if this is as good as the fully end - to - end training case , as illustrated by in the top right example . An investigation of this issue remains future work .
We have analysed the ResNet architecture , in terms of the ensemble classifiers therein and the effective depths of the residual units . On the basis of that analysis we calculated a new , more spatially efficient , and better performing architecture which actually achieves fully end - to - end training for large networks . Using this new architecture we designed a group of correspondingly shallow networks , and showed that they outperform the previous very deep residual networks not only on the ImageNet classification dataset , but also when applied to semantic image segmentation .
The success of our models is primarily attributed to extensive parameter sharing between the encoder and decoder parts of the architecture , coupled with the capability to learn expressive non - linear latent node representations from both local graph neighborhoods and explicit node features . Further , our novel architecture is capable of simultaneous multi - task learning of both link prediction and node classification in one efficient end - to - end training stage . Our work provides a useful framework to make accurate and meaningful predictions on a diverse set of complex graph structures for a wide range of real - world applications .
Instead we predict multiple camera pose hypotheses as well as the respective uncertainty for each prediction . Towards this aim , we use Bingham distributions , to model the orientation of the camera pose , and a multivariate Gaussian to model the position , with an end-to-end deep neural network . By incorporating a Winner-Takes-All training scheme , we finally obtain a mixture model that is well suited for explaining ambiguities in the scene , yet does not suffer from mode collapse , a common problem with mixture density networks .
We define this approach as variational synthesis of mechanisms . In addition , we also present an alternate end-to-end deep neural network architecture for variational synthesis of linkages . This end-to-end architecture is a conditional-VAE , which approximates the conditional distribution of linkage parameters with respect to a coupler trajectory distribution .
Unlike the existing techniques , we do not use any manually crafted feature , instead directly operate on Lagrangian fluid simulation data and use machine learned features . This paper presents a novel end-to-end deep learning neural network that can automatically generate a model for fluid animation based-on Lagrangian fluid simulation data . This approach synthesizes velocity fields with irregular Lagrangian data structure using neural network .
However , we argue that multi-step citywide passenger demands encapsulate both time-varying demand trends and global statuses , and hence are more beneficial to avoiding demand-service mismatching and developing effective vehicle distribution/scheduling strategies . In this paper , we propose an end-to-end deep neural network solution to the prediction task . We employ the encoder-decoder framework based on convolutional and ConvLSTM units to identify complex features that capture spatiotemporal influences and pickup-dropoff interactions on citywide passenger demands .
We develop a data driven approach and learn both the measurement matrix and the inverse reconstruction scheme instead of utilizing random linear projections as measurements and reconstruction via convex optimization with a given known sparsity in conventional CS framework . To achieve this goal , we have designed an end to end deep neural network structure consisting of fully connected layers with cascaded convolutional layers to be trained and tested over a publicly available image dataset . Results show that the proposed technique provides higher peak signal to noise ratio ( PSNR ) levels and hence learns better measurement matrices than that of the randomly selected or specifically designed for a known sparsity basis to reduce average coherence .
However , networks designed for these tasks typically require vast quantities of training data and long training periods to converge . We investigate the design rationale behind end-to-end driving network designs by proposing and comparing three small and computationally inexpensive deep end-to-end neural network models that generate driving control signals directly from input images . In contrast to prior work that segments the autonomous driving task , our models take on a novel approach to the autonomous driving problem by utilizing deep and thin Fully Convolutional Nets ( FCNs ) with recurrent neural nets and low parameter counts to tackle a complex end-to-end regression task predicting both steering and acceleration commands .
In this paper , we examine three types of uncertainty : model capacity uncertainty , intrinsic data uncertainty , and open set uncertainty , and review techniques that have been derived to address each one . We then introduce a unified hierarchical model , which combines methods from Bayesian inference , invertible latent density inference , and discriminative classification in a single end-to-end deep neural network topology to yield efficient per-sample uncertainty estimation . Our approach addresses all three uncertainty types and readily accommodates prior/base rates for binary detection .
Each code is composed of multiple discrete numbers , such as , where the value of each component is limited to a fixed range . We propose to directly learn the discrete codes in an end - to - end neural network by applying the Gumbel - softmax trick . Experiments show the compression rate achieves in a sentiment analysis task and in machine translation tasks without performance loss .
The authors propose an approach based on handcrafted features using a semantic parse of the question and scene analysis of the image combined in a latent - world Bayesian framework . More recently , several end - to - end deep neural networks that learn features directly from data have been applied to this problem . Most of these are directly adapted from captioning models , and utilize a recurrent LSTM network , which takes the question and Convolutional Neural Net ( CNN ) image features as input , and outputs the answer .
subsection : QAN for image set embedding In this paper , feature generation and aggregation module is implemented through an end - to - end convolutional neural network named QAN as shown in Fig . [ reference ] .
Weakly supervised object detection only requires supervision at image category level . Bilen and Vedaldi presents an end - to - end deep network for WSOD , in which final image classification score is the weighted sum of proposal scores , that is , each proposal contributes a percentage to the final image classification . The deep network can correctly classify image even only “ see ” a part of object , and as a result , the top ranking proposal may fail to meet the standard object detection requirement ( IoU 0.5 between ground truths and predicted boxes ) .
In this study , we propose a style - controllable Multi - source Abstractive Summarization model for QUEstion answering , called Masque . The model is an end - to - end deep neural network that can generate answers conditioned on a given style . Experiments with MS MARCO 2.1 show that our model achieved state - of - the - art performance on two tasks with different answer styles .
The contributions of this paper are mainly in three aspects . First , we propose a novel end - to - end neural network architecture that generates a 3D mesh model from a single RGB image . Second , we design a projection layer which incorporates perceptual image features into the 3D geometry represented by GCN .
Feature - based models represent a premise and a hypothesis by their unlexicalized and lexicalized features , such as - gram length and the real - valued feature of length difference , then train a classifier to perform relation classification . Recently , end - to - end neural network - based models have drawn worldwide attention since they have demonstrated excellent performance on quite a few NLP tasks including machine translation , natural language inference , etc . On the basis of their model structures , we can divide neural network - based models for NLI into two classes , sentence encoding models and sentence interaction - aggregation models .
While prior work has addressed intra - class variation using localization and segmentation techniques , inter - class similarity may also affect feature learning and reduce classification performance . In this work , we address this problem using a novel optimization procedure for the end - to - end neural network training on FGVC tasks . Our procedure , called Pairwise Confusion ( PC ) reduces overfitting by intentionally introducing confusion in the activations .
Their loss function is permutation - invariant as it incorporates the Hungarian algorithm to match candidate hypotheses to ground - truth instances . Inspired by their work , Romera propose an end - to - end recurrent network with convolutional LSTMs that sequentially outputs binary segmentation maps for each instance . Ren improve upon by adding a box network to confine segmentations within a local window and skip connections instead of graphical models to restore the resolution at the output .
In particular , the proposed CRF - RNN can be plugged in as a part of a traditional deep neural network : It is capable of passing on error differentials from its outputs to inputs during back - propagation based training of the deep network while learning CRF parameters . We demonstrate the use of this approach by utilizing it for the semantic segmentation task : we form an end - to - end trainable deep network by combining a fully convolutional neural network with the CRF - RNN . Our system achieves a new state - of - the - art on the popular Pascal VOC segmentation benchmark .
They simply suppose : ( 1 ) a hard rule among user behaviors ( e.g. , add-to-cart must come before purchase and after view ) ; ( 2 ) merge repeated behaviors into one ( e.g. , view several times is considered as view once only ) , thus failing to unveil user preferences from their real behaviors . To ease these issues , we , therefore , propose a novel end-to-end neural framework – TDRB , which automatically models the Temporal Dynamics and Repeated Behaviors to assist in capturing user preference , thus achieving more accurate recommendations . Empirical studies on three real-world datasets demonstrate the superiority of our proposed TDRB against other state-of-the-arts .
Conventional decompilers have the following major limitations : ( i ) they are only applicable to specific source-target language pair , hence incurs undesired development cost for new language tasks ; ( ii ) their output high-level code can not effectively preserve the correct functionality of the input binary ; ( iii ) their output program does not capture the semantics of the input and the reversed program is hard to interpret . To address the above problems , we propose Coda , the first end-to-end neural-based framework for code decompilation . Coda decomposes the decompilation task into two key phases : First , Coda employs an instruction type-aware encoder and a tree decoder for generating an abstract syntax tree ( AST ) with attention feeding during the code sketch generation stage .
In dialogue systems , the task of dialogue generation aims to generate utterances given previous utterances as contexts . Among various spectrums of dialogue generation approaches , end-to-end neural generation models have received an increase of attention . These end-to-end neural generation models are capable of generating natural-sounding sentences with a unified neural encoder-decoder network structure .
In recent years , all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition ( ASR ) tasks . However , most existing works focus on building ASR models where train and test data are drawn from the same domain .
Speech enhancement systems , which denoise and dereverberate distorted signals , are usually optimized based on signal reconstruction objectives including the maximum likelihood and minimum mean square error . However , emergent end-to-end neural methods enable to optimize the speech enhancement system with more application-oriented objectives . For example , we can jointly optimize speech enhancement and automatic speech recognition ( ASR ) only with ASR error minimization criteria .
Although more recent neural network-based methods attempt to integrate target information using attention mechanism , the performance improvement is rather limited due to the underuse of this information . To address this issue , we propose an endto- end neural model , TGMN-CR , which makes better use of target content information . Specifically , our model first learns conditional tweet representation with respect to specific target .
These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without deepening into the rationality and superiority of neural network design . In this paper , we delve into an effective end-to-end neural network structure for stronger feature expression and spatial correlation learning . Specifically , we propose a non-locally enhanced encoder-decoder network framework , which consists of a pooling indices embedded encoder-decoder network to efficiently learn increasingly abstract feature representation for more accurate rain streaks modeling while perfectly preserving the image detail .
We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers . We first investigate how end-to-end neural sequence models ( with pre-trained language model representations ) perform on document-level role filler extraction , as well as how the length of context captured affects the models ' performance . To dynamically aggregate information captured by neural representations learned at different levels of granularity ( e.g. , the sentence- and paragraph-level ) , we propose a novel multi-granularity reader .
Compared with MRC on a single passage , multi - passage MRC is more challenging , since we are likely to get multiple confusing answer candidates from different passages . To address this problem , we propose an end - to - end neural model that enables those answer candidates from different passages to verify each other based on their content representations . Specifically , we jointly train three modules that can predict the final answer based on three factors : the answer boundary , the answer content and the cross - passage answer verification .
Noise Injection Adaption: End-to-End ReRAM Crossbar Non-ideal Effect Adaption for Neural Network Mapping Differentiable Sparsification for Deep Neural Networks Deep Cascade Learning A Low-Power Deconvolutional Accelerator for Convolutional Neural Network Based Segmentation on FPGA: Abstract Only Two-stream collaborative network for multi-label chest X-ray Image classification with lung segmentation Training classifiers with limited data using the Radon cumulative distribution transform An Architecture for Deep, Hierarchical Generative Models Deep Autoencoder Learning for Relay-Assisted Cooperative Communication Systems Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision Text-Independent Speaker Verification Using Long Short-Term Memory Networks Recurrent Pixel Embedding for Instance Grouping Instance-Aware Semantic Segmentation via Multi-task Network Cascades Quality Aware Network for Set to Set Recognition Fused Text Segmentation Networks for Multi-oriented Scene Text Detection Holistic, Instance-Level Human Parsing Wider or Deeper: Revisiting the ResNet Model for Visual Recognition Learning to Make Predictions on Graphs with Autoencoders 6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference Computational Creativity Via Assisted Variational Synthesis of Mechanisms Using Deep Generative Models FluidsNet: End-to-end learning for Lagrangian fluid simulation Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks Data Driven Measurement Matrix Learning for Sparse Reconstruction Fast Recurrent Fully Convolutional Networks for Direct Perception in Autonomous Driving Principled Uncertainty Estimation for Deep Neural Networks Compressing Word Embeddings via Deep Compositional Code Learning Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Quality Aware Network for Set to Set Recognition Multiple Instance Detection Network with Online Instance Classifier Refinement Multi-style Generative Reading Comprehension Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images Attention Boosted Sequential Inference Model Pairwise Confusion for Fine-Grained Visual Classification Semantic Instance Segmentation with a Discriminative Loss Function Conditional Random Fields as Recurrent Neural Networks Modelling Temporal Dynamics and Repeated Behaviors for Recommendation A Neural-based Program Decompiler Hierarchical Variational Memory Network for Dialogue Generation RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions Speech Enhancement Using End-to-End Speech Recognition Objectives A Target-Guided Neural Memory Model for Stance Detection in Twitter Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
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DAC FPGA Medical Imaging: Image Processing NIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 24th International Conference on Pattern Recognition (ICPR) BMVC Pattern Recognit. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) WSDM DSW ICLR ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ACL ECCV ArXiv ECCV ArXiv 2015 IEEE International Conference on Computer Vision (ICCV) PAKDD (1) WWW WASPAA IJCNN ACM Multimedia ACL ACL
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