Our simulation results confirm that memory contention is widespread on large-scale machines ; our applications suggest that contention is usually caused by synchronized access to a range of addresses ( rather than to a single address ) . We show that block-column allocation , where each range of addresses is divided into cache lines , and each cache line is allocated to a separate memory module , can nearly eliminate this source of memory contention . As our main contribution , we compare block-column allocation to row-major allocation ( a common data allocation scheme ) and logarithmic broadcasting ( the standard software technique for alleviating memory contention ) . Algorithms are described here for all the functions within the multiple hypothesis approach . These algorithms are then benchmarked using the distributed array of processors ( DAP ) series from Active Memory Technology , Inc. Results of these benchmarks show that the multiple hypothesis tracking algorithms can be successfully implemented on array processors , displaying processing times that increase sublinearly with the number of objects under surveillance . We describe the architecture and design of a back-end object manager , designed as an “ active memory ” system on a plugin board for a standard workstation ( or personal computer ) . We show how , with minimal modification to existing code , it is possible to achieve significant performance improvement for the execution of data-intensive methods on objects , simply by using our back-end object manager . However , way before their actual design and implementation , these emerging hybrid memory systems need to be simulated and analyzed to fully understand their capabilities and limitations . As the conventional architectural-level memory simulators require significant amounts of computational costs and time , we propose to utilize ML techniques for developing various memory-response models that can instantly provide a predicted response corresponding to any new memory configuration . Specifically , in this work , we apply four supervised ML techniques to build regression models for memory latency , bandwidth , power , and total read/write responses . Recently , immunology inspiration has been more flourishing in helping to put up the wonderful defense , basically in securing ad hoc networks . Imperfect detection , anomaly detection , memory response , diversity , and adaptive detection , all are some of the immune features that offer a worthy concept for solving security problems . The essence of the immune system is the ability to discriminate between the self ( legitimate users , corrupted data , etc . ) , and nonself ( unauthorized users , viruses etc . ) , moreover , all these attractive features amazed the researcher from which immune inspiration comes . By feeding back , in each layer of the coding system , the data processing means present in the layer via memory means , only so-called difference pictures are coded , which increases the efficiency considerably . The decoding system should then similarly be provided with fed-back memory means and , as a result , normally attains a very complex scope , which is avoided , however , if in the coding system coupling between two adjacent layers takes place in the highest layer thereof with two different points of the feedback loop . An alternate , more recent theory posits that short term memory can be supported by feedforward networks . While feedback driven memory can be implemented by well described mechanisms of synaptic plasticity , little is known of possible molecular and cellular mechanisms that can implement feedforward driven memory . Here we report such a mechanism in which the memory trace exists in the form of glutamate-bound but Mg2 + -blocked NMDA receptors on the thin terminal dendrites of CA1 pyramidal neurons . This process is referred to here as dendritic hold and read ( DHR ) . Because the read-out of the input is not dependent on repetition of the input and information flows in a single-pass manner , DHR can potentially support a feedforward memory architecture . This paper presents Feedback Memory Network ( \textttFMN ) which models user interactions with the search engine for query suggestion . Besides modeling the queries issued by the user , \textttFMN also considers user feedback on the search results . Binary neural associative memories are attractive for image processing because of their speed of operation for learning associations and for recalling them . We have added feedback to a feedforward associative memory , to produce a pattern completion network which performs translation-invariant pattern completion , and at the same time resolves contradictory image information . The image completion is realised as a complete labelling of the features comprising known compound objects in the image . Among these features , the PE significantly increases in frontal lobes . We suggest this feature in memory-based neurofeedback system to improve memory performance . As shown in Figure 1 , a ReasoNet is composed of the following components : Memory : The external memory is denoted as M. It is a list of word vectors , M = { This approach exploits the larger state - space and richer dynamics of RNNs compared to HMMs , and avoids the problem of using potentially incorrect alignments as training targets . The combination of Long Short - term Memory , an RNN architecture with an improved memory , with end - to - end training has proved especially effective for cursive handwriting recognition . However it has so far made little impact on speech recognition . Compared with the conventional " front-end"–"back-end"– " vocoder " structure , based on the attention mechanism , end-to-end speech synthesis systems directly train and synthesize from text sequence to the acoustic feature sequence as a whole . Recently , a more calculation efficient end-to-end architecture named transformer , which is solely based on self-attention , was proposed to model global dependencies between the input and output sequences . There is a rich and varied literature on the topic , going from state-space abstractions that clump similar states together to hierarchical approaches that define either temporally-extended actions or task subdivisions . This chapter reviews previous RL abstraction approaches so we can later position our attention focus algorithms with respect to the existing literature.ion is one of the most common ways of scaling up reinforcement learning , along with function approximation and often overlapping with it . There is a rich and varied literature on the topic , going from state-space abstractions that clump similar states together to hierarchical approaches that define either temporally-extended actions or task subdivisions . To this end , we propose a novel semisupervised inference algorithm named parameterized label propagation . In our model , the propagation weights between mentions are learned by an attention‐like mechanism , given their local contexts and the initial labels as input . We study the performance of our approach in the Yahoo ! This paper utilizes an attention concept approach in text mining to address the deficiencies of existing keyword search engines . We show how an attention concept in conjunction with a traditional search approach can be used to develop an adaptive text mining model with user-oriented , time-based and attentive knowledge . Without changing a user 's search behavior , this paper considers some specific post-search operations as attentive targets for building the personalized interest base . To correctly answer visual questions about an image , the machine needs to understand both the image and question . Recently , visual attention based models have been explored for VQA , where the attention mechanism typically produces a spatial map highlighting image regions relevant to answering the question . So far , all attention models for VQA in literature have focused on the problem of identifying ‘ The recent availability of large - scale annotated datasets has enabled researchers to develop various deep learning - based architectures for NLI . google2016 propose an attention - based model that decomposes the NLI task into sub - problems to solve them in parallel . They further show the benefit of adding intra - sentence attention to input representations . The attention architecture has several interesting properties : ( 1 ) the operation is efficient , since it is parallelizable across nodeneighbor pairs ; ( 2 ) it can be applied to graph nodes having different degrees by specifying arbitrary weights to the neighbors ; and ( 3 ) the model is directly applicable to inductive learning problems , including tasks where the model has to generalize to completely unseen graphs . We validate the proposed approach on four challenging benchmarks : Cora , Citeseer and Pubmed citation networks as well as an inductive protein - protein interaction dataset , achieving or matching state - of - the - art results that highlight the potential of attention - based models when dealing with arbitrarily structured graphs . It is worth noting that , as [ reference ] and [ reference ] , our work can also be reformulated as a particular instance of document : Attention - Based Models for Speech Recognition Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks including machine translation , handwriting synthesis and image caption generation . This paper proposes a target response adaptive correlation filter tracker with spatial attention to solve the above problems . Firstly , more useful feature information can be learned by making full use of the context information of the target area , and spatial attention mechanisms can be introduced to suppress the background information and reduce the unnecessary boundary effect . Secondly , the dynamic change of the target response is captured , and the most reliable response map is selected when the interference response map appears , to reduce the probability of dispositioning and train the more recognizable filter . Our framework uses a triplet classification network as a base network . We propose to use a spatial attention module and combine high-level and mid-level output features of CNN to represent the input images . Moreover , we design a novel domain-invariant feature by using a gradient reverse layer ( GRL ) to solve the domain gap problem . We assign attention coefficients to each edge and then aggregate the point features as a weighted sum of its neighbors . The learned LAE-Conv layer features are then given to a point-wise spatial attention module to generate an interdependency matrix of all points regardless of their distances , which captures long-range spatial contextual features contributing to more precise semantic information . The proposed point attention network consists of an encoder and decoder which , together with the LAE-Conv layers and the point-wise spatial attention modules , make it an end-to-end trainable network for predicting dense labels for 3D point cloud segmentation . Experiments on challenging benchmarks of 3D point clouds show that our algorithm can perform at par or better than the existing state of the art methods . The effectiveness of the proposed approach is validated on the salient object dataset . The experimental results show that the CGED process can obviously improve the performance of salient object detection compared with the input spatial attention map , and the proposed approach can achieve performance comparable to that of state-of-the-art approaches . The deployment of spatial attention is highly sensitive to stimulus predictability . Despite evidence for strong crossmodal links in spatial attentional systems , it remains to be elucidated how concurrent but divergent predictions for targets in different sensory modalities are integrated . In a series of behavioral studies , we investigated the processing of modality-specific expectancies using a multimodal cueing paradigm in which auditory cues predicted the location of visual or tactile targets with modality-specific cue predictability . To remedy the above problems , in this paper , we introduce the Spatial-/Channel-wise Attention Models into the traditional Regression CNN to estimate the density map , which is named as “ SCAR ” . It consists of two modules , namely Spatial-wise Attention Model ( SAM ) and Channel-wise Attention Model ( CAM ) . The former can encode the pixel-wise context of the entire image to more accurately predict density maps at the pixel level . To address the above two limitations , this paper proposes the two-stream collaborative learning with spatial-temporal attention ( TCLSTA ) approach , which consists of two models . First , for the spatial-temporal attention model , the spatial-level attention emphasizes the salient regions in a frame , and the temporal-level attention exploits the discriminative frames in a video . They are mutually enhanced to jointly learn the discriminative static and motion features for better classification performance . The entire model is trained end - to - end and the evidence for the computed answer can be visualized using the attention weights . To summarize our contributions , in this paper we propose a novel multi - hop memory network with spatial attention for the VQA task which allows one to visualize the spatial inference process used by the deep network ( a CAFFE implementation will be made available ) , design an attention architecture in the first hop which uses each word embedding to capture fine - grained alignment between the image and question , create a series of synthetic questions that explicitly require spatial inference to analyze the working principles of the network , and show that it learns logical inference rules by visualizing the attention weights , provide an extensive evaluation of several existing models and our own model on the same publicly available datasets . Sec . In this paper , we propose a novel stereoscopic image retargeting algorithm based on 3D visual saliency detection . A new 3D visual attention model is designed based on 2D visual feature detection , depth feature detection and the modeling of various viewing bias in stereo vision . A geometrically consistent seam carving technique is adopted for retargeting stereo image pair . Although saliency detection is well explored for 2D image and video content , there are only few attempts made to design 3D saliency prediction models . Newly proposed 3D visual attention models have to be validated over large-scale video saliency prediction datasets , which also contain results of eye-tracking information . There are several publicly available eye-tracking datasets for 2D image and video content . In this paper , we propose the attention identical dual network ( AID-Net ) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels . To leverage the performance , 3D attention mechanisms were integrated into the AID-Net to provide complementary information for classification tasks . Moreover , the 3D Gradient-weighted Class Activation Mapping ( Grad-CAM ) was also proposed at the testing stage to interpret the behaviors of the deep neural network . Firstly , a novel 3D FCN structure is introduced to replace 2D FCNs as well as to improve current 3D convolutional neural networks ( CNNs ) by providing a mean to learn distinctive spatiotemporal representations of each crop type from the reshaped multi-temporal images . Secondly , to strengthen the learning significance of the spatiotemporal representations , our approach includes 3D channel attention modules , which regulate the between-channel consistency of the features from the encoder and the decoder , and a 3D global pooling module , which selects the most distinctive features at the top of the encoder . Experiments were conducted using two data sets with different types of crops and time spans .
Our simulation results confirm that memory contention is widespread on large-scale machines ; our applications suggest that contention is usually caused by synchronized access to a range of addresses ( rather than to a single address ) . We show that block-column allocation , where each range of addresses is divided into cache lines , and each cache line is allocated to a separate memory module , can nearly eliminate this source of memory contention . As our main contribution , we compare block-column allocation to row-major allocation ( a common data allocation scheme ) and logarithmic broadcasting ( the standard software technique for alleviating memory contention ) .
Algorithms are described here for all the functions within the multiple hypothesis approach . These algorithms are then benchmarked using the distributed array of processors ( DAP ) series from Active Memory Technology , Inc. Results of these benchmarks show that the multiple hypothesis tracking algorithms can be successfully implemented on array processors , displaying processing times that increase sublinearly with the number of objects under surveillance .
We describe the architecture and design of a back-end object manager , designed as an “ active memory ” system on a plugin board for a standard workstation ( or personal computer ) . We show how , with minimal modification to existing code , it is possible to achieve significant performance improvement for the execution of data-intensive methods on objects , simply by using our back-end object manager .
However , way before their actual design and implementation , these emerging hybrid memory systems need to be simulated and analyzed to fully understand their capabilities and limitations . As the conventional architectural-level memory simulators require significant amounts of computational costs and time , we propose to utilize ML techniques for developing various memory-response models that can instantly provide a predicted response corresponding to any new memory configuration . Specifically , in this work , we apply four supervised ML techniques to build regression models for memory latency , bandwidth , power , and total read/write responses .
Recently , immunology inspiration has been more flourishing in helping to put up the wonderful defense , basically in securing ad hoc networks . Imperfect detection , anomaly detection , memory response , diversity , and adaptive detection , all are some of the immune features that offer a worthy concept for solving security problems . The essence of the immune system is the ability to discriminate between the self ( legitimate users , corrupted data , etc . ) , and nonself ( unauthorized users , viruses etc . ) , moreover , all these attractive features amazed the researcher from which immune inspiration comes .
By feeding back , in each layer of the coding system , the data processing means present in the layer via memory means , only so-called difference pictures are coded , which increases the efficiency considerably . The decoding system should then similarly be provided with fed-back memory means and , as a result , normally attains a very complex scope , which is avoided , however , if in the coding system coupling between two adjacent layers takes place in the highest layer thereof with two different points of the feedback loop .
An alternate , more recent theory posits that short term memory can be supported by feedforward networks . While feedback driven memory can be implemented by well described mechanisms of synaptic plasticity , little is known of possible molecular and cellular mechanisms that can implement feedforward driven memory . Here we report such a mechanism in which the memory trace exists in the form of glutamate-bound but Mg2 + -blocked NMDA receptors on the thin terminal dendrites of CA1 pyramidal neurons .
This process is referred to here as dendritic hold and read ( DHR ) . Because the read-out of the input is not dependent on repetition of the input and information flows in a single-pass manner , DHR can potentially support a feedforward memory architecture .
This paper presents Feedback Memory Network ( \textttFMN ) which models user interactions with the search engine for query suggestion . Besides modeling the queries issued by the user , \textttFMN also considers user feedback on the search results .
Binary neural associative memories are attractive for image processing because of their speed of operation for learning associations and for recalling them . We have added feedback to a feedforward associative memory , to produce a pattern completion network which performs translation-invariant pattern completion , and at the same time resolves contradictory image information . The image completion is realised as a complete labelling of the features comprising known compound objects in the image .
Among these features , the PE significantly increases in frontal lobes . We suggest this feature in memory-based neurofeedback system to improve memory performance .
As shown in Figure 1 , a ReasoNet is composed of the following components : Memory : The external memory is denoted as M. It is a list of word vectors , M = {
This approach exploits the larger state - space and richer dynamics of RNNs compared to HMMs , and avoids the problem of using potentially incorrect alignments as training targets . The combination of Long Short - term Memory , an RNN architecture with an improved memory , with end - to - end training has proved especially effective for cursive handwriting recognition . However it has so far made little impact on speech recognition .
Compared with the conventional " front-end"–"back-end"– " vocoder " structure , based on the attention mechanism , end-to-end speech synthesis systems directly train and synthesize from text sequence to the acoustic feature sequence as a whole . Recently , a more calculation efficient end-to-end architecture named transformer , which is solely based on self-attention , was proposed to model global dependencies between the input and output sequences .
There is a rich and varied literature on the topic , going from state-space abstractions that clump similar states together to hierarchical approaches that define either temporally-extended actions or task subdivisions . This chapter reviews previous RL abstraction approaches so we can later position our attention focus algorithms with respect to the existing literature.ion is one of the most common ways of scaling up reinforcement learning , along with function approximation and often overlapping with it . There is a rich and varied literature on the topic , going from state-space abstractions that clump similar states together to hierarchical approaches that define either temporally-extended actions or task subdivisions .
To this end , we propose a novel semisupervised inference algorithm named parameterized label propagation . In our model , the propagation weights between mentions are learned by an attention‐like mechanism , given their local contexts and the initial labels as input . We study the performance of our approach in the Yahoo !
This paper utilizes an attention concept approach in text mining to address the deficiencies of existing keyword search engines . We show how an attention concept in conjunction with a traditional search approach can be used to develop an adaptive text mining model with user-oriented , time-based and attentive knowledge . Without changing a user 's search behavior , this paper considers some specific post-search operations as attentive targets for building the personalized interest base .
To correctly answer visual questions about an image , the machine needs to understand both the image and question . Recently , visual attention based models have been explored for VQA , where the attention mechanism typically produces a spatial map highlighting image regions relevant to answering the question . So far , all attention models for VQA in literature have focused on the problem of identifying ‘
The recent availability of large - scale annotated datasets has enabled researchers to develop various deep learning - based architectures for NLI . google2016 propose an attention - based model that decomposes the NLI task into sub - problems to solve them in parallel . They further show the benefit of adding intra - sentence attention to input representations .
The attention architecture has several interesting properties : ( 1 ) the operation is efficient , since it is parallelizable across nodeneighbor pairs ; ( 2 ) it can be applied to graph nodes having different degrees by specifying arbitrary weights to the neighbors ; and ( 3 ) the model is directly applicable to inductive learning problems , including tasks where the model has to generalize to completely unseen graphs . We validate the proposed approach on four challenging benchmarks : Cora , Citeseer and Pubmed citation networks as well as an inductive protein - protein interaction dataset , achieving or matching state - of - the - art results that highlight the potential of attention - based models when dealing with arbitrarily structured graphs . It is worth noting that , as [ reference ] and [ reference ] , our work can also be reformulated as a particular instance of
document : Attention - Based Models for Speech Recognition Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks including machine translation , handwriting synthesis and image caption generation .
This paper proposes a target response adaptive correlation filter tracker with spatial attention to solve the above problems . Firstly , more useful feature information can be learned by making full use of the context information of the target area , and spatial attention mechanisms can be introduced to suppress the background information and reduce the unnecessary boundary effect . Secondly , the dynamic change of the target response is captured , and the most reliable response map is selected when the interference response map appears , to reduce the probability of dispositioning and train the more recognizable filter .
Our framework uses a triplet classification network as a base network . We propose to use a spatial attention module and combine high-level and mid-level output features of CNN to represent the input images . Moreover , we design a novel domain-invariant feature by using a gradient reverse layer ( GRL ) to solve the domain gap problem .
We assign attention coefficients to each edge and then aggregate the point features as a weighted sum of its neighbors . The learned LAE-Conv layer features are then given to a point-wise spatial attention module to generate an interdependency matrix of all points regardless of their distances , which captures long-range spatial contextual features contributing to more precise semantic information . The proposed point attention network consists of an encoder and decoder which , together with the LAE-Conv layers and the point-wise spatial attention modules , make it an end-to-end trainable network for predicting dense labels for 3D point cloud segmentation . Experiments on challenging benchmarks of 3D point clouds show that our algorithm can perform at par or better than the existing state of the art methods .
The effectiveness of the proposed approach is validated on the salient object dataset . The experimental results show that the CGED process can obviously improve the performance of salient object detection compared with the input spatial attention map , and the proposed approach can achieve performance comparable to that of state-of-the-art approaches .
The deployment of spatial attention is highly sensitive to stimulus predictability . Despite evidence for strong crossmodal links in spatial attentional systems , it remains to be elucidated how concurrent but divergent predictions for targets in different sensory modalities are integrated . In a series of behavioral studies , we investigated the processing of modality-specific expectancies using a multimodal cueing paradigm in which auditory cues predicted the location of visual or tactile targets with modality-specific cue predictability .
To remedy the above problems , in this paper , we introduce the Spatial-/Channel-wise Attention Models into the traditional Regression CNN to estimate the density map , which is named as “ SCAR ” . It consists of two modules , namely Spatial-wise Attention Model ( SAM ) and Channel-wise Attention Model ( CAM ) . The former can encode the pixel-wise context of the entire image to more accurately predict density maps at the pixel level .
To address the above two limitations , this paper proposes the two-stream collaborative learning with spatial-temporal attention ( TCLSTA ) approach , which consists of two models . First , for the spatial-temporal attention model , the spatial-level attention emphasizes the salient regions in a frame , and the temporal-level attention exploits the discriminative frames in a video . They are mutually enhanced to jointly learn the discriminative static and motion features for better classification performance .
The entire model is trained end - to - end and the evidence for the computed answer can be visualized using the attention weights . To summarize our contributions , in this paper we propose a novel multi - hop memory network with spatial attention for the VQA task which allows one to visualize the spatial inference process used by the deep network ( a CAFFE implementation will be made available ) , design an attention architecture in the first hop which uses each word embedding to capture fine - grained alignment between the image and question , create a series of synthetic questions that explicitly require spatial inference to analyze the working principles of the network , and show that it learns logical inference rules by visualizing the attention weights , provide an extensive evaluation of several existing models and our own model on the same publicly available datasets . Sec .
In this paper , we propose a novel stereoscopic image retargeting algorithm based on 3D visual saliency detection . A new 3D visual attention model is designed based on 2D visual feature detection , depth feature detection and the modeling of various viewing bias in stereo vision . A geometrically consistent seam carving technique is adopted for retargeting stereo image pair .
Although saliency detection is well explored for 2D image and video content , there are only few attempts made to design 3D saliency prediction models . Newly proposed 3D visual attention models have to be validated over large-scale video saliency prediction datasets , which also contain results of eye-tracking information . There are several publicly available eye-tracking datasets for 2D image and video content .
In this paper , we propose the attention identical dual network ( AID-Net ) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels . To leverage the performance , 3D attention mechanisms were integrated into the AID-Net to provide complementary information for classification tasks . Moreover , the 3D Gradient-weighted Class Activation Mapping ( Grad-CAM ) was also proposed at the testing stage to interpret the behaviors of the deep neural network .
Firstly , a novel 3D FCN structure is introduced to replace 2D FCNs as well as to improve current 3D convolutional neural networks ( CNNs ) by providing a mean to learn distinctive spatiotemporal representations of each crop type from the reshaped multi-temporal images . Secondly , to strengthen the learning significance of the spatiotemporal representations , our approach includes 3D channel attention modules , which regulate the between-channel consistency of the features from the encoder and the decoder , and a 3D global pooling module , which selects the most distinctive features at the top of the encoder . Experiments were conducted using two data sets with different types of crops and time spans .
Alleviating Memory Contention in Matrix Computations on Large-Scale Shared-Memory Multiprocessors Multiple hypothesis tracking algorithms for massively parallel computers The Design of a Back-end Object Management System Machine learning based design space exploration for hybrid main-memory design Biologically Inspired Model for Securing Hybrid Mobile Ad hoc Networks Coding and decoding a system and apparatus for a digital signal Dendritic Hold and Read: A Gated Mechanism for Short Term Information Storage and Retrieval Query Suggestion with Feedback Memory Network Neural associative processing of document images Analysis of Regularity in the EEG Before/After Working Memory Task ReasoNet: Learning to Stop Reading in Machine Comprehension Speech recognition with deep recurrent neural networks Enhancing Hybrid Self-attention Structure with Relative-position-aware Bias for Speech Synthesis Leveraging attention focus for effective reinforcement learning in complex domains Collective Named Entity Recognition in User Comments via Parameterized Label Propagation An attentive self-organizing neural model for text mining Hierarchical Question-Image Co-Attention for Visual Question Answering DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference Graph Attention Networks Attention-Based Models for Speech Recognition A target response adaptive correlation filter tracker with spatial attention Learning multi-level domain invariant features for sketch re-identification Point Attention Network for Semantic Segmentation of 3D Point Clouds Cauchy graph embedding based diffusion model for salient object detection. Integrating modality-specific expectancies for the deployment of spatial attention SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting Two-Stream Collaborative Learning With Spatial-Temporal Attention for Video Classification Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Stereoscopic image retargeting based on 3D saliency detection Benchmark 3D eye-tracking dataset for visual saliency prediction on stereoscopic 3D video Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images
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Code Generation MEMSYS WWW CoCo@NIPS 2013 IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP NIPS NAACL-HLT ICLR NIPS ECCV ICASSP Medical Imaging: Image Processing
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