Attention mechanism advances the neural machine translation ( NMT ) by reducing the confusion introduced by irrelevant words in long sentences . However , the confusion caused by ambiguous words has n't been handled yet and it may be a bottleneck for the NMT model . 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 . 4.1 Hierarchical Abstractions Hierarchical RL attacks a learning problem by dividing it into different levels of complexity . 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 . We extend the attention - mechanism with features needed for speech recognition . Finally , we introduce a modification of the attention mechanism to avoid concentrating the attention on a single frame , and thus avoid obtaining less “ effective training examples ” , bringing the PER down to 17.6 % . section : Attention - Based Model for Speech Recognition subsection : General Framework subsection : Details of Evaluated Models We evaluated the ARSGs with different attention mechanisms . The encoder was a 3 - layer BiRNN with 256 GRU units in each direction , and the activations of the 512 top - layer units were used as the representation . We also leverage memory and attention to empower a recurrent network with stronger memorization capability and more importantly the ability to discover relations among tokens . This is realized by inserting a memory network module in the update of a recurrent network together with attention for memory addressing . The attention acts as a weak inductive module discovering relations between input tokens , and is trained without direct supervision . In the former task our best model outperforms even all previously reported ensembles . We are excited about the future of attention - based models and plan to apply them to other tasks . We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local , restricted attention mechanisms to efficiently handle large inputs and outputs such as images , audio and video . Compared with the conventional object matching schemes , the proposed technique provides a more general and compact formulation of the problem and a solution more suitable for parallel implementation . At the coarse search stage , the surface matching rates between the input image and each object model in the database are computed through a Hopfield network and used to select the candidates for further consideration . At the fine search stage , the object models selected from the previous stage are fed into another Hopfield network for vertex matching . Because the development of ANNs involves not only science but also experience , the paper presents various intuitive yet effective ways of mapping resource leveling problems on different applicable ANN architectures . To demonstrate the application of ANNs to resource leveling , a simple ANN model is developed using a Hopfield network . The conclusion highlights the usefulness and the limitations of ANNs when applied to resource leveling problems . Optical implementations of neural networks based on the Hopfield model have always found it difficult to produce the negative weights required for the interconnecting synaptic matrix . One solution involves the addition of a positive offset to the weights to ensure that they all become non-negative but this introduces another problem as a dynamic ( or time-dependent ) threshold value is then required which may be difficult to implement . * The Hopfield Model * Beyond the Hopfield Model * Hidden Units The computational complexity of the algorithmic solution to the minimisation of this error is at times incompatible with certain particularly critical industrial applications . To reduce the calculation time , the author presents a neural approach based on a Hopfield-type model . A detailed definition of the neural approach is given , its capacity for solving the problem is demonstrated , and the computational complexity is analysed . While non-adaptive ( Hopfield ) networks were the first neural networks to be used for this problem , their practical limitations and dependence on hard-to-determine parameters motivated investigation into dramatically different neural approaches . The new approaches exploit adaptive neural networks , and outperform Hopfield type approaches by a substantial amount , but usually require thousands of iterations and additional mechanisms to ensure generation of a valid tour . The guilty net , an even more recent adaptive approach , utilizes a straightforward competitive learning algorithm with fixed neighbors and “ conscience ” to automatically generate valid , short tours in only a few hundred iterations . Another drawback pointed out in the literature is that some of these neural approaches fail to converge efficiently towards feasible equilibrium points . This paper discusses the application of a modified Hopfield architecture for solving ED problems defined by nonlinear cost function . The internal parameters of the neural network adopted here are computed using the valid-subspace technique , which guarantees convergence to equilibrium points that represent a solution for the ED problem . The Hopfield architecture can be utilized in the VLSI implementation of several important optimization functions . A description is given of the properties of local minima in the energy function of Hopfield networks . In the present paper , the completely innovative architecture of artificial neural network based on Hopfield structure for solving a stereo-matching problem — hybrid neural network , consisting of the classical analog Hopfield neural network and the Maximum Neural Network — is described . The application of this kind of structure as a part of assistive device for visually impaired individuals is considered . In the present paper we describe completely innovative of architecture of artificial neural network based on Hopfield structure . It is analogue implementation of dual mode Hopfield-like network for solving stereo matching problem . To effectively decompose such mixtures , a fully constrained linear unmixing algorithm based on a multichannel Hopfield neural network ( MHNN ) is proposed in this letter . The proposed MHNN algorithm is actually a Hopfield-based architecture which handles all the pixels in an image synchronously , instead of considering a per-pixel procedure . Due to the synchronous unmixing property of MHNN , a noise energy percentage ( NEP ) stopping criterion which utilizes the signal-to-noise ratio is proposed to obtain optimal results for different applications automatically . We have selected as application of our approach the shortest path problem . For this problem , the complexity of the neural network grows as the number of nodes in a graph , instead of the square of it and the converged rate is decreased in an order of magnitude respect to previous Hopfield based approaches . Further optimization can be achieved using a bank of nets , without affecting the speed of the whole algorithm , and allowing a trade-off with the complexity used . However , the autocorrelation performance of some well known Hyperchaos sequences , such as Hopfield sequence , is poor , and their applications are limited . Based on recently presented APAS theorem , we have modified the Hopfield map to Symmetrical-Hopfield(S-Hopfield ) map , and the autocorrelation performance of the S-Hopfield sequence is excellent , whereas other performances of the S-Hopfield sequence remain invariant or become better , and this sequence can be used more widely . Taking advantage of FGMOSFET 's properties , this work shows an analog CMOS circuit that finds the best solution of the combinatorial task of assignments in terms of Lagrange multipliers . This is in contrast to the Hopfield paradigm , which is often VLSI-costly . The building block in this circuit uses FGMOSFET transistors , has low silicon complexity and exhibits good electrical performance according to PSpice simulations . A method for the manipulator position control was previously proposed by K. Tsutsumi et al. ( 1987 ) on the basis of the Hopfield scheme for neural computation . According to the proposed method , the manipulator is modeled not by using polar coordinates but by using the distances between the joint locations alone . Second , we propose a novel self-voting-based article attention layer to extract salient information shared by multiple articles . We show that models that incorporate this attention layer are robust to potential noises in news stories and outperform existing baselines on both clean and noisy datasets . We further enhance our model by incorporating human labels , and show that our distant supervision approach significantly reduces the demand on labeled data . However , research into the L2 proficiency attentional network by integrating three dimensions of the cognitive interference tasks has not much reported . In the present study , Stroop task , Simon task and Flanker task are combined to increase the dimensions of the attentional network . Behavioral results show that the performance difference between English majors and non-English majors consists in the second position of the interference trials with the accuracy rate of English majors much higher and latency much lower than those of non-English majors , confirming the hypothesis that higher L2 proficiency is positively correlated to attentional network . The fronto-parietal network was less active during post-task rest compared to pre-task rest , and regional CBF decrease in this network correlated with performance decline . These results demonstrate the persistent effects of cognitive fatigue in the fronto-parietal network after a period of heavy mental work and indicate the critical role of this attentional network in mediating TOT effects . Furthermore , resting regional CBF in the thalamus and right middle frontal gyrus prior to task onset was predictive of subjects ' subsequent performance decline , suggesting that resting CBF quantified by ASL perfusion fMRI may be a useful indicator of performance potential and a marker of the level of fatigue in the neural attentional system . Our method combines multitask learning on action selection and an auxiliary binary classification objective , together with a convolutional neural network architecture that uses an attentional mechanism to avoid distractors . We show that pairing interaction data from just a single environment with a diverse dataset of weakly labeled data results in greatly improved generalization to unseen environments , and show that this generalization depends on both the auxiliary objective and the attentional architecture that we propose . We demonstrate our results in both simulation and on a real robotic manipulator , and demonstrate substantial improvement over standard convolutional architectures and domain adaptation methods . Here , we propose a novel Attention Graph Convolution Network ( AGCN ) to perform superpixel-wise segmentation in big SAR imagery data . AGCN consists of an attention mechanism layer and Graph Convolution Networks ( GCN ) . GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification . Psychophysiological Interaction ( PPI ) analysis revealed extensive emotion-specific functional connectivity of AC with insula , cingulate cortex , as well as with visual , and parietal attentional structures . These findings show that the auditory cortex functions as a central hub of an affective-attentional network that is more extensive than previously believed . PPI analyses also showed functional connectivity of SF with AC during the joy condition , taken to reflect that SF is sensitive to social signals with positive valence . Critically , the CDA switched polarity when M2 displays appeared on the opposite side . In line with the attentional activation account , these reversed CDA components exclusively reflected the number of items that were encoded from M2 displays , irrespective of how many M1 items were already held in working memory . On trials where M1 and M2 displays were presented on the same side and on trials where M2 displays appeared nonlaterally , CDA components elicited in the interval after M2 remained sensitive to a residual trace of M1 items , indicating that some activation of previously stored items was maintained across encoding episodes . Owing to large-scale diversity and location uncertainty in object detection , how to enrich semantic information has become an important issue that attracts a lot of concern . In this study , the authors propose a novel attentional detection network ( ADN ) to enrich semantic information of feature maps by adding an extra attention branch to the classic detection network . Compared to previous methods ( e.g. feature pyramid network ( FPN ) , single shot multibox detector ( SSD ) ) that producing massive anchors in different layers of feature maps to detect objects with different scales and aspect ratios , which is very time-consuming , their network is lightweight and do not need to produce extra anchors . Qualitative experiments demonstrated the use of this attentional model on a robotic humanoid platform and in a real-time manner control the overt attention of the robot by specifying the saccadic movements of the robot head . These experiments also exposed another highly important aspect of the model ; its temporal variability , as opposed to many other attentional ( saliency ) models that exclusively deal with static images . Here the dynamic aspects of the attentional mechanism proved to allow for a temporally varying trade-off between top-down and bottom-up influences depending on changes in the environment of the robot . It ’s necessary to consider whether each orientation ( forward or backward ) has different importance on word encoding , thus adaptively joint the two orientation vectors together with different weights . Therefore , in this study , using ESIM model as the baseline , we add an attention layer behind each Bi - LSTM layer , then use an adaptive orientation embedding layer to jointly represent the forward and backward vectors . We name this attention boosted Bi - LSTM as Bi - aLSTM , and denote the modified ESIM as aESIM . Next , a bi - directional attention is used to compute the interacted representation and . where is column - wise normalized function . We use the same attention layer to model the interactions for all the three levels , and get the final fused representation for the question and passage respectively . Note that while dealing with the attention output of the universal node , we added two outputs from passage - to - question attention and question - to - passage attention . To distinguish hidden states from different sources , we denote the of - th word in and the of - th word in as and respectively . Attention Layer Attention mechanism in previous RC tasks enables question - aware passage representations . We propose a novel attention mechanism inspired by word - by - word style attention methods . As this paper focuses on the faithfulness problem , we use the most popular decoder , i.e. , GRU with attentions . At each decoding time step , GRU reads the previous output and context vector as inputs to compute new hidden state : Since we have both sentence and relation representations as input , we develop two attentional layers to construct the overall context vector . For instance , the context representation of the sentence at time step is computed as : where MLP stands for multi - layer perceptrons . This paper proposes a tag labeling system based on self-attention generative adversarial networks ( SAGAN ) . It aims to add labels to heterogeneous data sources , use LDA method to cluster labels , and then introduce attention mechanism based SAGAN , explore the correspondence between semantic similarity and audio tags features , and find a feasible audio tags labeling method .
Attention mechanism advances the neural machine translation ( NMT ) by reducing the confusion introduced by irrelevant words in long sentences . However , the confusion caused by ambiguous words has n't been handled yet and it may be a bottleneck for the NMT model .
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 . 4.1 Hierarchical Abstractions Hierarchical RL attacks a learning problem by dividing it into different levels of complexity .
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 . We extend the attention - mechanism with features needed for speech recognition .
Finally , we introduce a modification of the attention mechanism to avoid concentrating the attention on a single frame , and thus avoid obtaining less “ effective training examples ” , bringing the PER down to 17.6 % . section : Attention - Based Model for Speech Recognition subsection : General Framework
subsection : Details of Evaluated Models We evaluated the ARSGs with different attention mechanisms . The encoder was a 3 - layer BiRNN with 256 GRU units in each direction , and the activations of the 512 top - layer units were used as the representation .
We also leverage memory and attention to empower a recurrent network with stronger memorization capability and more importantly the ability to discover relations among tokens . This is realized by inserting a memory network module in the update of a recurrent network together with attention for memory addressing . The attention acts as a weak inductive module discovering relations between input tokens , and is trained without direct supervision .
In the former task our best model outperforms even all previously reported ensembles . We are excited about the future of attention - based models and plan to apply them to other tasks . We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local , restricted attention mechanisms to efficiently handle large inputs and outputs such as images , audio and video .
Compared with the conventional object matching schemes , the proposed technique provides a more general and compact formulation of the problem and a solution more suitable for parallel implementation . At the coarse search stage , the surface matching rates between the input image and each object model in the database are computed through a Hopfield network and used to select the candidates for further consideration . At the fine search stage , the object models selected from the previous stage are fed into another Hopfield network for vertex matching .
Because the development of ANNs involves not only science but also experience , the paper presents various intuitive yet effective ways of mapping resource leveling problems on different applicable ANN architectures . To demonstrate the application of ANNs to resource leveling , a simple ANN model is developed using a Hopfield network . The conclusion highlights the usefulness and the limitations of ANNs when applied to resource leveling problems .
Optical implementations of neural networks based on the Hopfield model have always found it difficult to produce the negative weights required for the interconnecting synaptic matrix . One solution involves the addition of a positive offset to the weights to ensure that they all become non-negative but this introduces another problem as a dynamic ( or time-dependent ) threshold value is then required which may be difficult to implement .
* The Hopfield Model * Beyond the Hopfield Model * Hidden Units
The computational complexity of the algorithmic solution to the minimisation of this error is at times incompatible with certain particularly critical industrial applications . To reduce the calculation time , the author presents a neural approach based on a Hopfield-type model . A detailed definition of the neural approach is given , its capacity for solving the problem is demonstrated , and the computational complexity is analysed .
While non-adaptive ( Hopfield ) networks were the first neural networks to be used for this problem , their practical limitations and dependence on hard-to-determine parameters motivated investigation into dramatically different neural approaches . The new approaches exploit adaptive neural networks , and outperform Hopfield type approaches by a substantial amount , but usually require thousands of iterations and additional mechanisms to ensure generation of a valid tour . The guilty net , an even more recent adaptive approach , utilizes a straightforward competitive learning algorithm with fixed neighbors and “ conscience ” to automatically generate valid , short tours in only a few hundred iterations .
Another drawback pointed out in the literature is that some of these neural approaches fail to converge efficiently towards feasible equilibrium points . This paper discusses the application of a modified Hopfield architecture for solving ED problems defined by nonlinear cost function . The internal parameters of the neural network adopted here are computed using the valid-subspace technique , which guarantees convergence to equilibrium points that represent a solution for the ED problem .
The Hopfield architecture can be utilized in the VLSI implementation of several important optimization functions . A description is given of the properties of local minima in the energy function of Hopfield networks .
In the present paper , the completely innovative architecture of artificial neural network based on Hopfield structure for solving a stereo-matching problem — hybrid neural network , consisting of the classical analog Hopfield neural network and the Maximum Neural Network — is described . The application of this kind of structure as a part of assistive device for visually impaired individuals is considered .
In the present paper we describe completely innovative of architecture of artificial neural network based on Hopfield structure . It is analogue implementation of dual mode Hopfield-like network for solving stereo matching problem .
To effectively decompose such mixtures , a fully constrained linear unmixing algorithm based on a multichannel Hopfield neural network ( MHNN ) is proposed in this letter . The proposed MHNN algorithm is actually a Hopfield-based architecture which handles all the pixels in an image synchronously , instead of considering a per-pixel procedure . Due to the synchronous unmixing property of MHNN , a noise energy percentage ( NEP ) stopping criterion which utilizes the signal-to-noise ratio is proposed to obtain optimal results for different applications automatically .
We have selected as application of our approach the shortest path problem . For this problem , the complexity of the neural network grows as the number of nodes in a graph , instead of the square of it and the converged rate is decreased in an order of magnitude respect to previous Hopfield based approaches . Further optimization can be achieved using a bank of nets , without affecting the speed of the whole algorithm , and allowing a trade-off with the complexity used .
However , the autocorrelation performance of some well known Hyperchaos sequences , such as Hopfield sequence , is poor , and their applications are limited . Based on recently presented APAS theorem , we have modified the Hopfield map to Symmetrical-Hopfield(S-Hopfield ) map , and the autocorrelation performance of the S-Hopfield sequence is excellent , whereas other performances of the S-Hopfield sequence remain invariant or become better , and this sequence can be used more widely .
Taking advantage of FGMOSFET 's properties , this work shows an analog CMOS circuit that finds the best solution of the combinatorial task of assignments in terms of Lagrange multipliers . This is in contrast to the Hopfield paradigm , which is often VLSI-costly . The building block in this circuit uses FGMOSFET transistors , has low silicon complexity and exhibits good electrical performance according to PSpice simulations .
A method for the manipulator position control was previously proposed by K. Tsutsumi et al. ( 1987 ) on the basis of the Hopfield scheme for neural computation . According to the proposed method , the manipulator is modeled not by using polar coordinates but by using the distances between the joint locations alone .
Second , we propose a novel self-voting-based article attention layer to extract salient information shared by multiple articles . We show that models that incorporate this attention layer are robust to potential noises in news stories and outperform existing baselines on both clean and noisy datasets . We further enhance our model by incorporating human labels , and show that our distant supervision approach significantly reduces the demand on labeled data .
However , research into the L2 proficiency attentional network by integrating three dimensions of the cognitive interference tasks has not much reported . In the present study , Stroop task , Simon task and Flanker task are combined to increase the dimensions of the attentional network . Behavioral results show that the performance difference between English majors and non-English majors consists in the second position of the interference trials with the accuracy rate of English majors much higher and latency much lower than those of non-English majors , confirming the hypothesis that higher L2 proficiency is positively correlated to attentional network .
The fronto-parietal network was less active during post-task rest compared to pre-task rest , and regional CBF decrease in this network correlated with performance decline . These results demonstrate the persistent effects of cognitive fatigue in the fronto-parietal network after a period of heavy mental work and indicate the critical role of this attentional network in mediating TOT effects . Furthermore , resting regional CBF in the thalamus and right middle frontal gyrus prior to task onset was predictive of subjects ' subsequent performance decline , suggesting that resting CBF quantified by ASL perfusion fMRI may be a useful indicator of performance potential and a marker of the level of fatigue in the neural attentional system .
Our method combines multitask learning on action selection and an auxiliary binary classification objective , together with a convolutional neural network architecture that uses an attentional mechanism to avoid distractors . We show that pairing interaction data from just a single environment with a diverse dataset of weakly labeled data results in greatly improved generalization to unseen environments , and show that this generalization depends on both the auxiliary objective and the attentional architecture that we propose . We demonstrate our results in both simulation and on a real robotic manipulator , and demonstrate substantial improvement over standard convolutional architectures and domain adaptation methods .
Here , we propose a novel Attention Graph Convolution Network ( AGCN ) to perform superpixel-wise segmentation in big SAR imagery data . AGCN consists of an attention mechanism layer and Graph Convolution Networks ( GCN ) . GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification .
Psychophysiological Interaction ( PPI ) analysis revealed extensive emotion-specific functional connectivity of AC with insula , cingulate cortex , as well as with visual , and parietal attentional structures . These findings show that the auditory cortex functions as a central hub of an affective-attentional network that is more extensive than previously believed . PPI analyses also showed functional connectivity of SF with AC during the joy condition , taken to reflect that SF is sensitive to social signals with positive valence .
Critically , the CDA switched polarity when M2 displays appeared on the opposite side . In line with the attentional activation account , these reversed CDA components exclusively reflected the number of items that were encoded from M2 displays , irrespective of how many M1 items were already held in working memory . On trials where M1 and M2 displays were presented on the same side and on trials where M2 displays appeared nonlaterally , CDA components elicited in the interval after M2 remained sensitive to a residual trace of M1 items , indicating that some activation of previously stored items was maintained across encoding episodes .
Owing to large-scale diversity and location uncertainty in object detection , how to enrich semantic information has become an important issue that attracts a lot of concern . In this study , the authors propose a novel attentional detection network ( ADN ) to enrich semantic information of feature maps by adding an extra attention branch to the classic detection network . Compared to previous methods ( e.g. feature pyramid network ( FPN ) , single shot multibox detector ( SSD ) ) that producing massive anchors in different layers of feature maps to detect objects with different scales and aspect ratios , which is very time-consuming , their network is lightweight and do not need to produce extra anchors .
Qualitative experiments demonstrated the use of this attentional model on a robotic humanoid platform and in a real-time manner control the overt attention of the robot by specifying the saccadic movements of the robot head . These experiments also exposed another highly important aspect of the model ; its temporal variability , as opposed to many other attentional ( saliency ) models that exclusively deal with static images . Here the dynamic aspects of the attentional mechanism proved to allow for a temporally varying trade-off between top-down and bottom-up influences depending on changes in the environment of the robot .
It ’s necessary to consider whether each orientation ( forward or backward ) has different importance on word encoding , thus adaptively joint the two orientation vectors together with different weights . Therefore , in this study , using ESIM model as the baseline , we add an attention layer behind each Bi - LSTM layer , then use an adaptive orientation embedding layer to jointly represent the forward and backward vectors . We name this attention boosted Bi - LSTM as Bi - aLSTM , and denote the modified ESIM as aESIM .
Next , a bi - directional attention is used to compute the interacted representation and . where is column - wise normalized function . We use the same attention layer to model the interactions for all the three levels , and get the final fused representation for the question and passage respectively . Note that while dealing with the attention output of the universal node , we added two outputs from passage - to - question attention and question - to - passage attention .
To distinguish hidden states from different sources , we denote the of - th word in and the of - th word in as and respectively . Attention Layer Attention mechanism in previous RC tasks enables question - aware passage representations . We propose a novel attention mechanism inspired by word - by - word style attention methods .
As this paper focuses on the faithfulness problem , we use the most popular decoder , i.e. , GRU with attentions . At each decoding time step , GRU reads the previous output and context vector as inputs to compute new hidden state : Since we have both sentence and relation representations as input , we develop two attentional layers to construct the overall context vector . For instance , the context representation of the sentence at time step is computed as : where MLP stands for multi - layer perceptrons .
This paper proposes a tag labeling system based on self-attention generative adversarial networks ( SAGAN ) . It aims to add labels to heterogeneous data sources , use LDA method to cluster labels , and then introduce attention mechanism based SAGAN , explore the correspondence between semantic similarity and audio tags features , and find a feasible audio tags labeling method .
Multi-sense based neural machine translation Leveraging attention focus for effective reinforcement learning in complex domains Attention-Based Models for Speech Recognition Long Short-Term Memory-Networks for Machine Reading Attention is All you Need A connectionist approach to multiple-view based 3-D object recognition Potential of artificial neural networks for resource scheduling Implementation of the Hopfield model with excitatory and inhibitory synapses and static thresholding Complex Systems Dynamics A solution to the end-effector position optimisation problem in robotics using neural networks The guilty net for the traveling salesman problem Designing a modified Hopfield network to solve an economic dispatch problem with nonlinear cost function An investigation on local minima of a Hopfield network for optimization circuits A novel hybrid-maximum neural network in stereo-matching process A Novel Continuous Dual Mode Neural Network in Stereo-Matching Process Mixture Analysis by Multichannel Hopfield Neural Network A bank of Hopfield neural networks for the shortest path problem Autocorrelation Performance Improvement of Hopfield Hyperchaos Sequence Based on APAS Theorem Floating-Gate MOSFET parallel analog network for assignment problems Neural computation for controlling the configuration of 2-dimensional truss structure Generating Representative Headlines for News Stories Second Language Proficiency and Attentional Network: Evidence from the Multi-Source Interference Task (MSIT) Imaging brain fatigue from sustained mental workload: An ASL perfusion study of the time-on-task effect GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data The roles of superficial amygdala and auditory cortex in music-evoked fear and joy Does Contralateral Delay Activity Reflect Working Memory Storage or the Current Focus of Spatial Attention within Visual Working Memory? ADN for object detection Visual Attention in Active Vision Systems : Attending, Classifying and Manipulating Objects Attention Boosted Sequential Inference Model U-Net: Machine Reading Comprehension with Unanswerable Questions End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension Faithful to the Original: Fact Aware Neural Abstractive Summarization An Approach to Labeling Audio Tags Based on Self-Attention Generative Adversarial Networks
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IJCNN NIPS EMNLP NIPS COMPSAC ICNN ICANN (3) CSISE (2) CCE ICNN ICNC (6) ArXiv ArXiv AAAI
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