The effect of matrix normalization on the speed up ratio of pattern detection is discussed . Simulation results show that local submatrix normalization through weight normalization is faster than submatrix normalization in the spatial domain . The overall speed up ratio of the detection process is increased as the normalization of weights is done off line . The invention provides a particle filter weight processing and resampling method based on an FPGA . The particle filter weight processing and resampling method has the advantages that on one hand , a weight normalization process is abandoned , parallelization sorting processing is carried out on weights , all particles can be compared and exchanged by M/2 comparators and controllers within one period , and the comparison and exchanging efficiency of a sort algorithm is improved ; on the other hand , a threshold value of a resampling part is set according to permutation distribution of the weights , the large-weight particles , the middle-weight particles and the small-weight particles are determined through Nh and N1 , and the threshold value is not a preset threshold value . The large-weight particles and the middle-weight particles are reserved in the resampling process , the small-weight particles are eliminated , the weights and the position information of the large-weight particles are input into an FIFO , one circuit of the weights and the position information are output through an FIFOOUT to replace a weight of a small-weight particle register , the other circuit of the weights and the position information are input into the FIFO , and the function of copying the large-weight particles is achieved . The self-organizing neural network ( SONN ) for solving general " 0 - 1 " combinatorial optimization problems ( COPs ) is studied in this paper , with the aim of overcoming existing limitations in convergence and solution quality . This is achieved by incorporating two main features : an efficient weight normalization process exhibiting bifurcation dynamics , and neurons with additive noise . The SONN is studied both theoretically and experimentally by using the N-queen problem as an example to demonstrate and explain the dependence of optimization performance on annealing schedules and other system parameters . Extensive experiments indicate that Linger compares favourably to other classification techniques . We study the effects of various feature selection , weighting and normalization methods , and also the portability of the anti-spam filter across different users . The experiments show that it is more accurate than several other techniques . We also investigate the effects of various feature selection , weighting and normalization methods , and also the portability of the anti-spam filter across different users . This paper presents an adaptive large neighborhood search algorithm ( ALNS ) , combined with a speed optimization procedure , to solve the bi-objective PRP . Using the ALNS as the search engine , four a posteriori methods , namely the weighting method , the weighting method with normalization , the epsilon-constraint method and a new hybrid method ( HM ) , are tested using a scalarization of the two objective functions . The HM combines adaptive weighting with the epsilon-constraint method . The model does not attempt to take into account all that is known about the development of the visual system . Rather , its aim is to show how simple Hebbian learning combined with a suitable weight normalization rule can lead to the development of these mappings . The model forms an appropriate mapping when presented with distributed patterns of activity in the retina . This leads to an interesting question of whether controlling these quantities might improve the generalization behaviour of NNs . To this end , we propose stable rank normalization ( SRN ) , a novel , optimal , and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator . Surprisingly we find that SRN , inspite of being non-convex problem , can be shown to have a unique optimal solution . Based on the research , a HOQ has been produced with 9 kinds of attributes . With weighting normalization on the highest priority attributes to be developed , it has a value of 13,31 and the lowest is 8,79 . As for the parameters on the tool , it can be seen from the highest normalized contribution value of 17.84 , which means that the number shows the most important priority parameters . We discuss each experiment separately in the following subsections . The baselines we consider are ( i ) uniform scaling : minimizing a uniformly weighted sum of loss functions 1 T t L t , ( ii ) single task : solving tasks independently , ( iii ) grid search : exhaustively trying various values from { c t ∈ [ 0 , 1 ] | t c t = 1 } and optimizing for [ reference ] : using the uncertainty weighting proposed by [ reference ] , and ( v ) GradNorm : using the normalization proposed by [ reference ] . section : MultiMNIST We also envision that our methods could be applied to regression - type learning tasks . [ reference ] . All data layers were initialized following [ reference ] , and we applied weight normalization and mean - only batch normalization [ reference ] with momentum 0.999 to all of them . We used leaky ReLU [ reference ] with α = 0.1 as the non - linearity , and chose to use max pooling instead of strided convolutions because it gave consistently better results in our experiments . Gradients are normalized by the number of non - padding tokens per mini - batch . We also use weight normalization for all layers except for lookup tables [ reference ] . Besides dropout on the embeddings and the decoder output , we also apply dropout to the input of the convolutional blocks To further improve the propagation of training signal , we use deep residual networks DBLP : journals / corr / HeZRS15 , DBLP : journals / corr / HeZR016 with batch normalization ioffe2015batch and weight normalization badrinarayanan2015understanding , salimans2016weight in and . As described in Appendix [ reference ] we introduce and use a novel variant of batch normalization which is based on a running average over recent minibatches , and is thus more robust when training with very small minibatches . In this paper , a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers . Furthermore , a new and full resolution DCNN - Atrous Convolutional Neural Network ( ACNN ) , which incorporates cascaded atrous II-blocks , residual learning and Instance Normalization ( IN ) is proposed . Application results of the proposed ACNN to Magnetic Resonance Imaging ( MRI ) and Computed Tomography ( CT ) image segmentation demonstrate that the proposed ACNN can achieve higher segmentation Intersection over Unions ( IoUs ) than U-Net and Deeplabv3 + , but with reduced trainable parameters . Although these variations have been deemed to be implicitly handled by more training data and deeper networks , recent advances in image style transfer suggest that it is also possible to explicitly manipulate the style information . Extending this idea to general visual recognition problems , we present Batch-Instance Normalization ( BIN ) to explicitly normalize unnecessary styles from images . Considering certain style features play an essential role in discriminative tasks , BIN learns to selectively normalize only disturbing styles while preserving useful styles . Finally , the acceleration work of the CGAN-based crowd counting module is carried out . The generator of CGAN ( Conditional Generative Adversarial Network ) was optimized such as residual layer pruning , upsampling optimization , and instance normalization layer removing , and then deployed and INT8 quantized in TensorRT . After these optimizations , the inferring speed on the NVIDIA platform is increased to 541.6 % of the original network with almost no loss of inference accuracy . The framework proposes a new kind of more meaningful and informative units named frequent Deep Dependency Sub-Structure ( DDSS ) and a topic-sensitive Multi-Task Learning ( MTL ) model for frequent DDSS ranking . Given a document set , first , we parse all the sentences into deep dependency structures with a Head-driven Phrase Structure Grammar ( HPSG ) parser and mine the frequent DDSSs after semantic normalization . Then we employ a topic-sensitive MTL model to learn the importance of these frequent DDSSs . We start with deep syntactic analysis of the text and , for each sentence , extract logical form triples , subject-predicate-object . We then apply cross-sentence pronoun resolution , co-reference resolution , and semantic normalization to refine the set of triples and merge them into a semantic graph . This procedure is applied to both documents and corresponding summary extracts . In this work , we analyzed some proteins belonging to ten different architectural families . After retrieval , feature selection and normalization stages , the dataset has been processed by means of simple correlation , partial correlation and principal component analysis ( PCA ) , highlighting family-independent or family-specific relationships , and possible outliers for the dataset itself . The results can be useful to connect these features to functional protein properties . In the wavelet approaches , texture is characterized by its energy distribution in the decomposed subbands . However it is unclear on how to define similarity functions on extracted features ; usually simple norm-based distances together with heuristic normalization are employed . We develop a novel wavelet-based texture retrieval method that is based on the modeling of the marginal distribution of wavelet coefficients using the generalized Gaussian density ( GGD ) and a closed form Kullback-Leibler distance between the GGDs . Finally , we investigate rectified linear units ( ReLUs ) activation function and cross-lingual pre-training in deep neural network ( DNN ) acoustic model training . In the STD procedure , we adopt term-dependent score normalization and combine the outputs of diverse ASR systems to increase actual term weighted value ( ATWV ) . After applying these methods , our current best single system achieves 48.32 % word accuracy and 0.398 ATWV after STD system combination on OpenKWS13 Vietnamese development set . We use the idea of pivoting with the well known cosine normalization function . We point out some shortcomings of the cosine function andpresent two new normalization functions -- pivoted unique normalization and piuotert byte size normalization . Experimental results show that the developed system operates very well on simulated data even with generic models . An additional pose normalization step reduces the impact of pose mismatch due to camera setup and pose variation , and hence improves the detection accuracy further . Cloud detection , removal and data prediction in cloudy region is essential for image interpretation . Approach : This study demonstrated applicability of empirical relative radiometric normalization met hods to a set of multitemporal cloudy images acquired by Resourcesat-1 LISS III sensor . Objectiv e of this study was to detect and remove cloud cover and normalize an image radiometrically . To support the Portuguese language PyPLN uses the PALAVRAS parser\citep{Bick2000}. Currently PyPLN offers the following features : Text extraction with encoding normalization ( to UTF-8 ) , part-of-speech tagging , token frequency , semantic annotation , n-gram extraction , word and sentence repertoire , and full-text search across corpora . The platform is licensed as GPL-v3 . To this end , a real-time optimal control approach is proposed using deep learning technologies to obtain minimum-time trajectories of solar sail spacecraft for orbit transfer missions . First , the minimum-time two-dimensional orbit transfer problem is solved by an indirect method , and the costate normalization technique is introduced to increase the probability of finding the optimal solutions . Second , by making novel use of deep learning technologies , three deep neural networks are designed and trained offline by the obtained optimal solutions to generate the guidance commands in real time during flight . We applied a two-stage ( pre-train + fine-tuning ) learning architecture to train and adjust the model ( using Flickr30k and labels from online search as additional data ) . During the search phase of the task we take a different strategy to generate five different runs by leveraging video-length normalization and controlling the training source . To keep computational and memory requirements low , dimensionality reduction is performed via an FC layer , where we pick the output dimensionality D to be 128 . The compact D - dimensional descriptor is then passed to a batch - normalization layer [ reference ] and L2 - normalized to form the final template representation x template . section : GhostVLAD : NetVLAD with ghost clusters The last one is the volumetric upsampling block , which consists of a volumetric deconvolution layer , volumetric batch normalization layer , and the activation function ( i.e. , ReLU ) . Adding the batch normalization layer and the activation function to the deconvolution layer helps to ease the learning procedure . The kernel size of the residual blocks is 3×3×3 and that of the downsampling and upsampling layers is 2×2×2 with stride 2 . Training deep learning models at petaFLOPS scale requires overcoming both algorithmic and systems software challenges . In this paper , we discuss three systems-related optimizations : ( 1 ) distributed batch normalization to control per-replica batch sizes , ( 2 ) input pipeline optimizations to sustain model throughput , and ( 3 ) 2-D torus all-reduce to speed up gradient summation . We combine these optimizations to train ResNet-50 on ImageNet to 76.3 % accuracy in 2.2 minutes on a 1024-chip TPU v3 Pod with a training throughput of over 1.05 million images/second and no accuracy drop . We provide baseline performances on both of the benchmarks and re-implement state-of-the-art models for open source . We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance . We show that the networks trained on ADE20 K are able to segment a wide variety of scenes and objects . Interestingly , training ResNeXt - 101 with an increased batch size results in similar accuracy to the deeper ( and computationally more expensive ) ResNeXt - 152 model . As an additional reference , we train ResNeXt - 101 with synchronized Batch Normalization ( InPlace - ABNsync ) , which can be seen as a ‘ ‘ virtual ’ ’ increase of batch size applied to the computation of BN statistics . In this case we only observe small accuracy improvements when compared to the baseline model . Therefore , for instance level recognition tasks , it was not possible to train with batch - normalization statistics computed on a single GPU . Methods like synchronized batch - normalization or training on GPUs have been proposed to alleviate this problem . Synchronized batch - normalization slows down training significantly and training on GPUs is also impractical for most people . Therefore , group normalization has been recently proposed so that instance level recognition tasks can benefit from another form of normalization in a low batch setting during training .
The effect of matrix normalization on the speed up ratio of pattern detection is discussed . Simulation results show that local submatrix normalization through weight normalization is faster than submatrix normalization in the spatial domain . The overall speed up ratio of the detection process is increased as the normalization of weights is done off line .
The invention provides a particle filter weight processing and resampling method based on an FPGA . The particle filter weight processing and resampling method has the advantages that on one hand , a weight normalization process is abandoned , parallelization sorting processing is carried out on weights , all particles can be compared and exchanged by M/2 comparators and controllers within one period , and the comparison and exchanging efficiency of a sort algorithm is improved ; on the other hand , a threshold value of a resampling part is set according to permutation distribution of the weights , the large-weight particles , the middle-weight particles and the small-weight particles are determined through Nh and N1 , and the threshold value is not a preset threshold value . The large-weight particles and the middle-weight particles are reserved in the resampling process , the small-weight particles are eliminated , the weights and the position information of the large-weight particles are input into an FIFO , one circuit of the weights and the position information are output through an FIFOOUT to replace a weight of a small-weight particle register , the other circuit of the weights and the position information are input into the FIFO , and the function of copying the large-weight particles is achieved .
The self-organizing neural network ( SONN ) for solving general " 0 - 1 " combinatorial optimization problems ( COPs ) is studied in this paper , with the aim of overcoming existing limitations in convergence and solution quality . This is achieved by incorporating two main features : an efficient weight normalization process exhibiting bifurcation dynamics , and neurons with additive noise . The SONN is studied both theoretically and experimentally by using the N-queen problem as an example to demonstrate and explain the dependence of optimization performance on annealing schedules and other system parameters .
Extensive experiments indicate that Linger compares favourably to other classification techniques . We study the effects of various feature selection , weighting and normalization methods , and also the portability of the anti-spam filter across different users .
The experiments show that it is more accurate than several other techniques . We also investigate the effects of various feature selection , weighting and normalization methods , and also the portability of the anti-spam filter across different users .
This paper presents an adaptive large neighborhood search algorithm ( ALNS ) , combined with a speed optimization procedure , to solve the bi-objective PRP . Using the ALNS as the search engine , four a posteriori methods , namely the weighting method , the weighting method with normalization , the epsilon-constraint method and a new hybrid method ( HM ) , are tested using a scalarization of the two objective functions . The HM combines adaptive weighting with the epsilon-constraint method .
The model does not attempt to take into account all that is known about the development of the visual system . Rather , its aim is to show how simple Hebbian learning combined with a suitable weight normalization rule can lead to the development of these mappings . The model forms an appropriate mapping when presented with distributed patterns of activity in the retina .
This leads to an interesting question of whether controlling these quantities might improve the generalization behaviour of NNs . To this end , we propose stable rank normalization ( SRN ) , a novel , optimal , and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator . Surprisingly we find that SRN , inspite of being non-convex problem , can be shown to have a unique optimal solution .
Based on the research , a HOQ has been produced with 9 kinds of attributes . With weighting normalization on the highest priority attributes to be developed , it has a value of 13,31 and the lowest is 8,79 . As for the parameters on the tool , it can be seen from the highest normalized contribution value of 17.84 , which means that the number shows the most important priority parameters .
We discuss each experiment separately in the following subsections . The baselines we consider are ( i ) uniform scaling : minimizing a uniformly weighted sum of loss functions 1 T t L t , ( ii ) single task : solving tasks independently , ( iii ) grid search : exhaustively trying various values from { c t ∈ [ 0 , 1 ] | t c t = 1 } and optimizing for [ reference ] : using the uncertainty weighting proposed by [ reference ] , and ( v ) GradNorm : using the normalization proposed by [ reference ] . section : MultiMNIST
We also envision that our methods could be applied to regression - type learning tasks . [ reference ] . All data layers were initialized following [ reference ] , and we applied weight normalization and mean - only batch normalization [ reference ] with momentum 0.999 to all of them . We used leaky ReLU [ reference ] with α = 0.1 as the non - linearity , and chose to use max pooling instead of strided convolutions because it gave consistently better results in our experiments .
Gradients are normalized by the number of non - padding tokens per mini - batch . We also use weight normalization for all layers except for lookup tables [ reference ] . Besides dropout on the embeddings and the decoder output , we also apply dropout to the input of the convolutional blocks
To further improve the propagation of training signal , we use deep residual networks DBLP : journals / corr / HeZRS15 , DBLP : journals / corr / HeZR016 with batch normalization ioffe2015batch and weight normalization badrinarayanan2015understanding , salimans2016weight in and . As described in Appendix [ reference ] we introduce and use a novel variant of batch normalization which is based on a running average over recent minibatches , and is thus more robust when training with very small minibatches .
In this paper , a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers . Furthermore , a new and full resolution DCNN - Atrous Convolutional Neural Network ( ACNN ) , which incorporates cascaded atrous II-blocks , residual learning and Instance Normalization ( IN ) is proposed . Application results of the proposed ACNN to Magnetic Resonance Imaging ( MRI ) and Computed Tomography ( CT ) image segmentation demonstrate that the proposed ACNN can achieve higher segmentation Intersection over Unions ( IoUs ) than U-Net and Deeplabv3 + , but with reduced trainable parameters .
Although these variations have been deemed to be implicitly handled by more training data and deeper networks , recent advances in image style transfer suggest that it is also possible to explicitly manipulate the style information . Extending this idea to general visual recognition problems , we present Batch-Instance Normalization ( BIN ) to explicitly normalize unnecessary styles from images . Considering certain style features play an essential role in discriminative tasks , BIN learns to selectively normalize only disturbing styles while preserving useful styles .
Finally , the acceleration work of the CGAN-based crowd counting module is carried out . The generator of CGAN ( Conditional Generative Adversarial Network ) was optimized such as residual layer pruning , upsampling optimization , and instance normalization layer removing , and then deployed and INT8 quantized in TensorRT . After these optimizations , the inferring speed on the NVIDIA platform is increased to 541.6 % of the original network with almost no loss of inference accuracy .
The framework proposes a new kind of more meaningful and informative units named frequent Deep Dependency Sub-Structure ( DDSS ) and a topic-sensitive Multi-Task Learning ( MTL ) model for frequent DDSS ranking . Given a document set , first , we parse all the sentences into deep dependency structures with a Head-driven Phrase Structure Grammar ( HPSG ) parser and mine the frequent DDSSs after semantic normalization . Then we employ a topic-sensitive MTL model to learn the importance of these frequent DDSSs .
We start with deep syntactic analysis of the text and , for each sentence , extract logical form triples , subject-predicate-object . We then apply cross-sentence pronoun resolution , co-reference resolution , and semantic normalization to refine the set of triples and merge them into a semantic graph . This procedure is applied to both documents and corresponding summary extracts .
In this work , we analyzed some proteins belonging to ten different architectural families . After retrieval , feature selection and normalization stages , the dataset has been processed by means of simple correlation , partial correlation and principal component analysis ( PCA ) , highlighting family-independent or family-specific relationships , and possible outliers for the dataset itself . The results can be useful to connect these features to functional protein properties .
In the wavelet approaches , texture is characterized by its energy distribution in the decomposed subbands . However it is unclear on how to define similarity functions on extracted features ; usually simple norm-based distances together with heuristic normalization are employed . We develop a novel wavelet-based texture retrieval method that is based on the modeling of the marginal distribution of wavelet coefficients using the generalized Gaussian density ( GGD ) and a closed form Kullback-Leibler distance between the GGDs .
Finally , we investigate rectified linear units ( ReLUs ) activation function and cross-lingual pre-training in deep neural network ( DNN ) acoustic model training . In the STD procedure , we adopt term-dependent score normalization and combine the outputs of diverse ASR systems to increase actual term weighted value ( ATWV ) . After applying these methods , our current best single system achieves 48.32 % word accuracy and 0.398 ATWV after STD system combination on OpenKWS13 Vietnamese development set .
We use the idea of pivoting with the well known cosine normalization function . We point out some shortcomings of the cosine function andpresent two new normalization functions -- pivoted unique normalization and piuotert byte size normalization .
Experimental results show that the developed system operates very well on simulated data even with generic models . An additional pose normalization step reduces the impact of pose mismatch due to camera setup and pose variation , and hence improves the detection accuracy further .
Cloud detection , removal and data prediction in cloudy region is essential for image interpretation . Approach : This study demonstrated applicability of empirical relative radiometric normalization met hods to a set of multitemporal cloudy images acquired by Resourcesat-1 LISS III sensor . Objectiv e of this study was to detect and remove cloud cover and normalize an image radiometrically .
To support the Portuguese language PyPLN uses the PALAVRAS parser\citep{Bick2000}. Currently PyPLN offers the following features : Text extraction with encoding normalization ( to UTF-8 ) , part-of-speech tagging , token frequency , semantic annotation , n-gram extraction , word and sentence repertoire , and full-text search across corpora . The platform is licensed as GPL-v3 .
To this end , a real-time optimal control approach is proposed using deep learning technologies to obtain minimum-time trajectories of solar sail spacecraft for orbit transfer missions . First , the minimum-time two-dimensional orbit transfer problem is solved by an indirect method , and the costate normalization technique is introduced to increase the probability of finding the optimal solutions . Second , by making novel use of deep learning technologies , three deep neural networks are designed and trained offline by the obtained optimal solutions to generate the guidance commands in real time during flight .
We applied a two-stage ( pre-train + fine-tuning ) learning architecture to train and adjust the model ( using Flickr30k and labels from online search as additional data ) . During the search phase of the task we take a different strategy to generate five different runs by leveraging video-length normalization and controlling the training source .
To keep computational and memory requirements low , dimensionality reduction is performed via an FC layer , where we pick the output dimensionality D to be 128 . The compact D - dimensional descriptor is then passed to a batch - normalization layer [ reference ] and L2 - normalized to form the final template representation x template . section : GhostVLAD : NetVLAD with ghost clusters
The last one is the volumetric upsampling block , which consists of a volumetric deconvolution layer , volumetric batch normalization layer , and the activation function ( i.e. , ReLU ) . Adding the batch normalization layer and the activation function to the deconvolution layer helps to ease the learning procedure . The kernel size of the residual blocks is 3×3×3 and that of the downsampling and upsampling layers is 2×2×2 with stride 2 .
Training deep learning models at petaFLOPS scale requires overcoming both algorithmic and systems software challenges . In this paper , we discuss three systems-related optimizations : ( 1 ) distributed batch normalization to control per-replica batch sizes , ( 2 ) input pipeline optimizations to sustain model throughput , and ( 3 ) 2-D torus all-reduce to speed up gradient summation . We combine these optimizations to train ResNet-50 on ImageNet to 76.3 % accuracy in 2.2 minutes on a 1024-chip TPU v3 Pod with a training throughput of over 1.05 million images/second and no accuracy drop .
We provide baseline performances on both of the benchmarks and re-implement state-of-the-art models for open source . We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance . We show that the networks trained on ADE20 K are able to segment a wide variety of scenes and objects .
Interestingly , training ResNeXt - 101 with an increased batch size results in similar accuracy to the deeper ( and computationally more expensive ) ResNeXt - 152 model . As an additional reference , we train ResNeXt - 101 with synchronized Batch Normalization ( InPlace - ABNsync ) , which can be seen as a ‘ ‘ virtual ’ ’ increase of batch size applied to the computation of BN statistics . In this case we only observe small accuracy improvements when compared to the baseline model .
Therefore , for instance level recognition tasks , it was not possible to train with batch - normalization statistics computed on a single GPU . Methods like synchronized batch - normalization or training on GPUs have been proposed to alleviate this problem . Synchronized batch - normalization slows down training significantly and training on GPUs is also impractical for most people . Therefore , group normalization has been recently proposed so that instance level recognition tasks can benefit from another form of normalization in a low batch setting during training .
New faster normalized neural networks for sub-matrix detection using cross correlation in the frequency domain and matrix decomposition Particle filter weight processing and resampling method based on FPGA A noisy self-organizing neural network with bifurcation dynamics for combinatorial optimization LINGER – A SMART PERSONAL ASSISTANT FOR E-MAIL CLASSIFICATION A neural network based approach to automated e-mail classification The bi-objective Pollution-Routing Problem Topography and ocular dominance can arise from distributed patterns of activity Stable Rank Normalization for Improved Generalization in Neural Networks and GANs Perancangan Alat Bantu Pengaduk Adonan Kerupuk Rambak dengan Metode Quality Function Deployment (Studi Kasus: UKM Rambak Nur Sragen) Multi-Task Learning as Multi-Objective Optimization Temporal Ensembling for Semi-Supervised Learning Convolutional Sequence to Sequence Learning Density estimation using Real NVP ACNN: a Full Resolution DCNN for Medical Image Segmentation Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks Design and Optimization of Crowd Behavior Analysis System Based on B/S Software Architecture Deep Dependency Substructure-Based Learning for Multidocument Summarization Learning Sub-structures of Document Semantic Graphs for Document Summarization Basic exploratory proteins analysis with statistical methods applied on structural features Texture similarity measurement using Kullback-Leibler distance on wavelet subbands The Vietnamese speech recognition based on rectified linear units deep neural network and spoken term detection system combination Pivoted document length normalization Detecting emotional stress from facial expressions for driving safety Radiometric Correction of Multitemporal Satellite Imagery PyPLN: a Distributed Platform for Natural Language Processing Real-Time Optimal Control for Spacecraft Orbit Transfer via Multiscale Deep Neural Networks A Two-stage Transfer Learning Approach for Storytelling Linking GhostVLAD for set-based face recognition V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map Image Classification at Supercomputer Scale Semantic Understanding of Scenes Through the ADE20K Dataset In-place Activated BatchNorm for Memory-Optimized Training of DNNs SNIPER: Efficient Multi-Scale Training
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Web Intelligence ICLR NeurIPS ICLR ICML ICLR IFTC ICIP ISCSLP SIGIR ICIP TRECVID ACCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NeurIPS
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