In this paper , we propose to apply Self-Attention GAN to further improve the performance of human pose estimation . With attention mechanism in the framework of GAN , we can learn long-range body joints dependencies , therefore enforce the entire body joints structural constrains to make all the body joints to be consistent . Our method outperforms other state-of-the-art methods on two standard benchmark datasets MPII and LSP for human pose estimation . A sentence could contain multiple sentiment-target pairs ; thus the main challenge of this task is to separate different opinion contexts for different targets . To this end , attention mechanism has played an important role in previous state-of-the-art neural models . The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations . This mechanism selects a candidate feature that is as close as possible to the semantics of the original text by calculating the probability of distribution of semantic information in the sentence . Furthermore , a new attention focus mechanism is designed , which iteratively updates the information weight to obtain another candidate feature that may affect the matching accuracy . Then , an optimal feature selection algorithm is designed by using the context vector and two candidate features . Further , in case of wild type , enhancement of optomotor responses to the stimulus with noise were observed , but not in short-term memory mutant . Recent studies predicted that short-term memory mutant has a defect in an attention-like mechanism that modulates a sensitivity of visual system . It is considered that the mechanism is closely related to the central brain . Taken together , we suggest that the modulation of visual system activity from the central brain exist in the wild type fly and this modulation is important for the regulation of the sensitivity of visual system . Further extensive analysis revealed more stable optomotor response in short-term memory mutant . This should reflect a defective in the mechanism , suggesting that the noise robustness is closely related to this attention-like mechanism . To solve this problem , we present an one-stage face detector , named Single Shot Attention-Based Face Detector ( AFD ) , which enables accurate detection of multi-scale faces with high efficiency , especially for small faces . Specifically , AFD consists of two inter-connected modules , namely attention proposal module ( APM ) and face detection module ( FDM ) . The former aims to generate the attention region and coarsely refine the anchors . The proposed deeply supervised traffic congestion detector has two modules : attention proposal module and deeply supervised inception network . Specifically , within the shallow layers , the binary edge/corner density features are used in attention proposal module to generate the rang of interest ( ROI ) mask automatically . This strategy keeps the training process focusing on the congestion features without disturbances . 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 . Various techniques have been proposed to produce fixed - length continuous word vectors based on characters or morphemes [ reference ][ reference ][ reference ][ reference ] . An effort to apply such techniques to NMT , parallel to ours , has found no significant improvement over word - based approaches [ reference ] . One technical difference from our work is that the attention mechanism still operates on the level of words in the model by [ reference ] , and that the representation of each word is fixed - length . We expect that the attention mechanism benefits from our variable - length representation : the network can learn to place attention on different subword units at each step . While keeping the model hyperparameters constant , the character - level models require additional parameters for the character composition and character embeddings . However , the attention - based model uses fewer parameters compared to the concatenation approach . When the two representations are concatenated , the overall word representation size is increased , which in turn increases the number of parameters required for the word - level bidirectional LSTM . Unlike the deep residual learning , MRN effectively learns the joint representation from vision and language information . The main idea is to use element - wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies . Various alternative models introduced by multimodality are explored based on our study . They use an attentional mechanism to handle two different information sources , a query and the context of the query ( e.g . contextual sentences or an image ) . The query is added to the output of the attentional module , that makes the attentional module learn the residual of query mapping as in deep residual learning . In this paper , we propose Multimodal Residual Networks ( MRN ) to learn multimodality of visual question - answering tasks exploiting the excellence of deep residual learning . It features convolutions , latent prior modeling , a Gaussian input distribution ( for natural images ) and , in some experiments , a multi - layer architecture . However , it does not use an explicit attentional mechanism . We note that even the single - layer version is already a deep generative model which can decide to process higher level information first before focusing on details , as we demonstrate to some degree . The algorithm is able to support both homogeneous and heterogeneous hardware systems , and dynamically adapts to the available hardware and user-specified process layout at run-time . As implemented in Open MPI , the LAMA provides 362,880 mapping permutations and is able to naturally scale out to additional hardware resources as they become available in future architectures . We evaluated our algorithm on a large compendium of 102 microarray studies published in the field of muscle development and disease , and compared it to similarity measures based on gene overlap and over-representation of biological processes assigned by GO.ResultsWhile the overlap in both genes and overrepresented GO-terms was poor , LAMA retrieved many more biologically meaningful links between studies , with substantially lower influence of technical factors . LAMA correctly grouped muscular dystrophy , regeneration and myositis studies , and linked patient and corresponding mouse model studies . LAMA also retrieves the connecting biological concepts . However , the complexity of the problem grows quickly with respect to the number of edges in the layered graph , which depends on the number of nodes , fibers , wavelengths , and multicast sessions . Hence , we propose two heuristics layered all-optical multicast algorithm [ ( LAMA ) and conservative fiber and wavelength assignment ( C-FWA ) ] to compare with CPLEX , existing work , and unicasting . Extensive computational experiments show that LAMA 's performance is very close to CPLEX , and it is significantly better than existing work and C-FWA for nearly all metrics , since LAMA jointly optimizes routing and fiber-wavelength assignment phases compared with the other candidates , which attack the problem by decomposing two phases . They all assume the existence of plan libraries , which require a lot of human efforts in predicting and formalizing plans and may be impractical in many cases . In this paper , we present a novel logic‐based approach to solve the multigoal recognition problem efficiently , without the need of plan libraries , using a state‐of‐the‐art heuristic search planner LAMA . In particular , we first propose the formulation of a multigoal recognition problem based on automated planning . Existing approaches to recognizing multiple-goal problems are probabilistic approaches assuming the existence of plan libraries , which require much human effort in predicting and formalizing plans , and may be unrealistic in many cases . In this paper , we present a novel logic-based approach to solve the multiple-goal problems efficiently , without the need of plan libraries , using a state-of-the-art heuristic search planner LAMA . In particular , we propose the first formulation of multiple-goal recognition problem based on planning , and present a two-level probabilistic plan recognition approach that deals with both concurrent and interleaving goals from observed activity sequences . In satisfacing planning , on the other hand , admissible heuristics are not exploited by the best-first search algorithms of existing planners even when a time window is available for improving the first solution found . For example , in the well-know planner LAMA , better solutions within such a time window are sought by restarting a Weighted-A * search guided by inadmissible heuristics , each time a better solution is found . It is unable to adapt when the demand changes , a problem known as slab calcification . This paper introduces locality-aware memory allocation ( LAMA ) , which addresses the problem by first analyzing the locality of Memcached ’s requests and then reassigning slabs to minimize the miss ratio or the average response time . By evaluating LAMA using various industry and academic workloads , the paper shows that LAMA outperforms existing techniques in the steady-state performance , the speed of convergence , and the ability to adapt to request pattern changes , and overcome slab calcification . It fails to adapt when the demand changes , a problem known as slab calcification . This paper introduces locality-aware memory allocation ( LAMA ) , which solves the problem by first analyzing the locality of the Memcached requests and then repartitioning the memory to minimize the miss ratio and the average response time . By evaluating LAMA using various industry and academic workloads , the paper shows that LAMA outperforms existing techniques in the steady-state performance , the speed of convergence , and the ability to adapt to request pattern changes and overcome slab calcification . Based on NCR , the node activation multiple access protocol ( NAMA ) elects nodes for collision-free broadcast transmissions over a single channel . The link activation multiple access ( LAMA ) , the pair-wise link activation multiple access ( PAMA ) , and the hybrid activation multiple access ( HAMA ) protocols operate over multiple channels that are orthogonal by codes or frequencies to elect either links or nodes for collision-free unicast transmissions , or a mix of broadcast and unicast transmissions . The throughput and delay characteristics of these protocols are studied by analysis and simulation in multihop wireless networks with randomly-generated topologies . We use high-level synthesis ( HLS ) to develop the VLSI architecture and provide corresponding FPGA implementation results . Our results demonstrate that the proposed decentralized LAMA equalizer achieves competitive performance and complexity as existing centralized solutions that have been designed on register-transfer level . The purpose of this article is to present a new aggregation operator of linguistic labels that uses the cardinality of these elements , the linguistic aggregation of majority additive ( LAMA ) operator . We also present an extension of the LAMA operator under the two‐tuple fuzzy linguistic representation model . © 2003 _ Die 2017 aktualisierten internationalen GOLD-Leitlinien zum Vorgehen bei COPD haben die Rolle der dualen Bronchodilatation mit LAMA plus LABA insgesamt aufgewertet : Bei schwerer Symptomlast kann sie bereits in der Patientengruppe B eingesetzt werden . For each sub-problem , we define a dedicated engine and a unified knowledge representation model . The implementation of the cognitive vision platform has been made with the LAMA platform , a software platform for the development of knowledge based systems , designed in the ORION team . To validate our cognitive vision platform , we have chosen a real world application : the early diagnosis of plant diseases . To overcome these difficulties , we present an Attention-based Fault-Tolerant ( FT-Attn ) algorithm which selects correct and relevant information for each agent at every time-step . The multi-head attention mechanism enables the agents to learn effective communication policies through experience concurrently to the action policies . Empirical results have shown that FT-Attn beats previous state-of-the-art methods in some complex environments and can adapt to various kinds of noisy environments without tuning the complexity of the algorithm . However , previous work achieves the value decomposition heuristically without valid theoretical groundings , where VDN supposes an additive formation and QMIX adopts an implicit inexplicable mixing method . In this paper , for the first time , we theoretically derive a linear decomposing formation from $ Q_{tot}$ to each $ Q^{i}$. Based on this theoretical finding , we introduce the multi-head attention mechanism to approximate each term in the decomposing formula with theoretical explanations . Experiments show that our method outperforms state-of-the-art MARL methods on the widely adopted StarCraft benchmarks across different scenarios , and attention analysis is also investigated with sights . Based on the definitions , we propose a novel Transformer based model for predicting study session dropout , DAS : Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment . DAS has an encoder-decoder structure which is composed of stacked multi-head attention and point-wise feed-forward networks . The deep attentive computations in DAS are capable of capturing complex relations among dynamic student interactions . However , most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment , as well as the utilization of emojis . To this end , we propose the emotion-semantic-enhanced bidirectional long short-term memory ( BiLSTM ) network with the multi-head attention mechanism model ( EBILSTM-MH ) for sentiment analysis . This model uses BiLSTM to learn feature representation of input texts , given the word embedding . We test and verify the proposed attention method on three language modeling tasks ( i.e. , PTB , WikiText-103 and One-billion ) and a neural machine translation task ( i.e. , WMT-2016 English-German ) . Multi-linear attention can not only largely compress the model parameters but also obtain performance improvements , compared with a number of language modeling approaches , such as Transformer , Transformer-XL , and Transformer with tensor train decomposition . One Write-Head is All You Need . Multi-head attention layers , as used in the Transformer neural sequence model , are a powerful alternative to RNNs for moving information across and between sequences . While training these layers is generally fast and simple , due to parallelizability across the length of the sequence , incremental inference ( where such paralleization is impossible ) is often slow , due to the memory-bandwidth cost of repeatedly loading the large " keys " and " values " tensors . In this work , we investigate the effect of utilizing information not only from the final layer but also from intermediate layers of a pre-trained language representation model to detect grammatical errors . We propose a multi-head multi-layer attention model that determines the appropriate layers in Bidirectional Encoder Representation from Transformers ( BERT ) . The proposed method achieved the best scores on three datasets for grammatical error detection tasks , outperforming the current state-of-the-art method by 6.0 points on FCE , 8.2 points on CoNLL14 , and 12.2 points on JFLEG in terms of F_0.5 . Besides improving performance , an advantage of using attention is that it can also help to interpret a model by showing how the model assigns weight to different input elements . However , the multi-layer , multi-head attention mechanism in the Transformer model can be difficult to decipher . To make the model more accessible , we introduce an open-source tool that visualizes attention at multiple scales , each of which provides a unique perspective on the attention mechanism . paragraph : Positional attention We also include an additional positional attention module in each decoder layer , which is a multi - head attention module with the same general attention mechanism used in other parts of the Transformer network , i.e. where is the model hidden size , but with the positional encoding as both query and key and the decoder states as the value . This incorporates positional information directly into the attention process and provides a stronger positional signal than the embedding layer alone . In this study , DeLight is interfaced with a heart rate variability biofeedback system with two modes for different purposes : stress intervention and relaxation assistance . We evaluated the prototype of DeLight in two user studies . The results of the first study show that DeLight has the potential for stress intervention ; the HRV biofeedback through the changes of ambient light could improve a user ’s awareness of stress and trigger behavioral conditioning , such as deep breathing . Physical viability , such as timing , memory , or energy efficiency , are standing challenges in realizing the true potential of DNNs . We propose DeLight , a set of novel methodologies which aim to bring physical constraints as design parameters in the training and execution of DNN architectures . We use physical profiling to bound the network size in accordance to the pertinent platform 's characteristics .
In this paper , we propose to apply Self-Attention GAN to further improve the performance of human pose estimation . With attention mechanism in the framework of GAN , we can learn long-range body joints dependencies , therefore enforce the entire body joints structural constrains to make all the body joints to be consistent . Our method outperforms other state-of-the-art methods on two standard benchmark datasets MPII and LSP for human pose estimation .
A sentence could contain multiple sentiment-target pairs ; thus the main challenge of this task is to separate different opinion contexts for different targets . To this end , attention mechanism has played an important role in previous state-of-the-art neural models . The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations .
This mechanism selects a candidate feature that is as close as possible to the semantics of the original text by calculating the probability of distribution of semantic information in the sentence . Furthermore , a new attention focus mechanism is designed , which iteratively updates the information weight to obtain another candidate feature that may affect the matching accuracy . Then , an optimal feature selection algorithm is designed by using the context vector and two candidate features .
Further , in case of wild type , enhancement of optomotor responses to the stimulus with noise were observed , but not in short-term memory mutant . Recent studies predicted that short-term memory mutant has a defect in an attention-like mechanism that modulates a sensitivity of visual system . It is considered that the mechanism is closely related to the central brain . Taken together , we suggest that the modulation of visual system activity from the central brain exist in the wild type fly and this modulation is important for the regulation of the sensitivity of visual system . Further extensive analysis revealed more stable optomotor response in short-term memory mutant . This should reflect a defective in the mechanism , suggesting that the noise robustness is closely related to this attention-like mechanism .
To solve this problem , we present an one-stage face detector , named Single Shot Attention-Based Face Detector ( AFD ) , which enables accurate detection of multi-scale faces with high efficiency , especially for small faces . Specifically , AFD consists of two inter-connected modules , namely attention proposal module ( APM ) and face detection module ( FDM ) . The former aims to generate the attention region and coarsely refine the anchors .
The proposed deeply supervised traffic congestion detector has two modules : attention proposal module and deeply supervised inception network . Specifically , within the shallow layers , the binary edge/corner density features are used in attention proposal module to generate the rang of interest ( ROI ) mask automatically . This strategy keeps the training process focusing on the congestion features without disturbances .
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 .
Various techniques have been proposed to produce fixed - length continuous word vectors based on characters or morphemes [ reference ][ reference ][ reference ][ reference ] . An effort to apply such techniques to NMT , parallel to ours , has found no significant improvement over word - based approaches [ reference ] . One technical difference from our work is that the attention mechanism still operates on the level of words in the model by [ reference ] , and that the representation of each word is fixed - length . We expect that the attention mechanism benefits from our variable - length representation : the network can learn to place attention on different subword units at each step .
While keeping the model hyperparameters constant , the character - level models require additional parameters for the character composition and character embeddings . However , the attention - based model uses fewer parameters compared to the concatenation approach . When the two representations are concatenated , the overall word representation size is increased , which in turn increases the number of parameters required for the word - level bidirectional LSTM .
Unlike the deep residual learning , MRN effectively learns the joint representation from vision and language information . The main idea is to use element - wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies . Various alternative models introduced by multimodality are explored based on our study .
They use an attentional mechanism to handle two different information sources , a query and the context of the query ( e.g . contextual sentences or an image ) . The query is added to the output of the attentional module , that makes the attentional module learn the residual of query mapping as in deep residual learning . In this paper , we propose Multimodal Residual Networks ( MRN ) to learn multimodality of visual question - answering tasks exploiting the excellence of deep residual learning .
It features convolutions , latent prior modeling , a Gaussian input distribution ( for natural images ) and , in some experiments , a multi - layer architecture . However , it does not use an explicit attentional mechanism . We note that even the single - layer version is already a deep generative model which can decide to process higher level information first before focusing on details , as we demonstrate to some degree .
The algorithm is able to support both homogeneous and heterogeneous hardware systems , and dynamically adapts to the available hardware and user-specified process layout at run-time . As implemented in Open MPI , the LAMA provides 362,880 mapping permutations and is able to naturally scale out to additional hardware resources as they become available in future architectures .
We evaluated our algorithm on a large compendium of 102 microarray studies published in the field of muscle development and disease , and compared it to similarity measures based on gene overlap and over-representation of biological processes assigned by GO.ResultsWhile the overlap in both genes and overrepresented GO-terms was poor , LAMA retrieved many more biologically meaningful links between studies , with substantially lower influence of technical factors . LAMA correctly grouped muscular dystrophy , regeneration and myositis studies , and linked patient and corresponding mouse model studies . LAMA also retrieves the connecting biological concepts .
However , the complexity of the problem grows quickly with respect to the number of edges in the layered graph , which depends on the number of nodes , fibers , wavelengths , and multicast sessions . Hence , we propose two heuristics layered all-optical multicast algorithm [ ( LAMA ) and conservative fiber and wavelength assignment ( C-FWA ) ] to compare with CPLEX , existing work , and unicasting . Extensive computational experiments show that LAMA 's performance is very close to CPLEX , and it is significantly better than existing work and C-FWA for nearly all metrics , since LAMA jointly optimizes routing and fiber-wavelength assignment phases compared with the other candidates , which attack the problem by decomposing two phases .
They all assume the existence of plan libraries , which require a lot of human efforts in predicting and formalizing plans and may be impractical in many cases . In this paper , we present a novel logic‐based approach to solve the multigoal recognition problem efficiently , without the need of plan libraries , using a state‐of‐the‐art heuristic search planner LAMA . In particular , we first propose the formulation of a multigoal recognition problem based on automated planning .
Existing approaches to recognizing multiple-goal problems are probabilistic approaches assuming the existence of plan libraries , which require much human effort in predicting and formalizing plans , and may be unrealistic in many cases . In this paper , we present a novel logic-based approach to solve the multiple-goal problems efficiently , without the need of plan libraries , using a state-of-the-art heuristic search planner LAMA . In particular , we propose the first formulation of multiple-goal recognition problem based on planning , and present a two-level probabilistic plan recognition approach that deals with both concurrent and interleaving goals from observed activity sequences .
In satisfacing planning , on the other hand , admissible heuristics are not exploited by the best-first search algorithms of existing planners even when a time window is available for improving the first solution found . For example , in the well-know planner LAMA , better solutions within such a time window are sought by restarting a Weighted-A * search guided by inadmissible heuristics , each time a better solution is found .
It is unable to adapt when the demand changes , a problem known as slab calcification . This paper introduces locality-aware memory allocation ( LAMA ) , which addresses the problem by first analyzing the locality of Memcached ’s requests and then reassigning slabs to minimize the miss ratio or the average response time . By evaluating LAMA using various industry and academic workloads , the paper shows that LAMA outperforms existing techniques in the steady-state performance , the speed of convergence , and the ability to adapt to request pattern changes , and overcome slab calcification .
It fails to adapt when the demand changes , a problem known as slab calcification . This paper introduces locality-aware memory allocation ( LAMA ) , which solves the problem by first analyzing the locality of the Memcached requests and then repartitioning the memory to minimize the miss ratio and the average response time . By evaluating LAMA using various industry and academic workloads , the paper shows that LAMA outperforms existing techniques in the steady-state performance , the speed of convergence , and the ability to adapt to request pattern changes and overcome slab calcification .
Based on NCR , the node activation multiple access protocol ( NAMA ) elects nodes for collision-free broadcast transmissions over a single channel . The link activation multiple access ( LAMA ) , the pair-wise link activation multiple access ( PAMA ) , and the hybrid activation multiple access ( HAMA ) protocols operate over multiple channels that are orthogonal by codes or frequencies to elect either links or nodes for collision-free unicast transmissions , or a mix of broadcast and unicast transmissions . The throughput and delay characteristics of these protocols are studied by analysis and simulation in multihop wireless networks with randomly-generated topologies .
We use high-level synthesis ( HLS ) to develop the VLSI architecture and provide corresponding FPGA implementation results . Our results demonstrate that the proposed decentralized LAMA equalizer achieves competitive performance and complexity as existing centralized solutions that have been designed on register-transfer level .
The purpose of this article is to present a new aggregation operator of linguistic labels that uses the cardinality of these elements , the linguistic aggregation of majority additive ( LAMA ) operator . We also present an extension of the LAMA operator under the two‐tuple fuzzy linguistic representation model . © 2003
_ Die 2017 aktualisierten internationalen GOLD-Leitlinien zum Vorgehen bei COPD haben die Rolle der dualen Bronchodilatation mit LAMA plus LABA insgesamt aufgewertet : Bei schwerer Symptomlast kann sie bereits in der Patientengruppe B eingesetzt werden .
For each sub-problem , we define a dedicated engine and a unified knowledge representation model . The implementation of the cognitive vision platform has been made with the LAMA platform , a software platform for the development of knowledge based systems , designed in the ORION team . To validate our cognitive vision platform , we have chosen a real world application : the early diagnosis of plant diseases .
To overcome these difficulties , we present an Attention-based Fault-Tolerant ( FT-Attn ) algorithm which selects correct and relevant information for each agent at every time-step . The multi-head attention mechanism enables the agents to learn effective communication policies through experience concurrently to the action policies . Empirical results have shown that FT-Attn beats previous state-of-the-art methods in some complex environments and can adapt to various kinds of noisy environments without tuning the complexity of the algorithm .
However , previous work achieves the value decomposition heuristically without valid theoretical groundings , where VDN supposes an additive formation and QMIX adopts an implicit inexplicable mixing method . In this paper , for the first time , we theoretically derive a linear decomposing formation from $ Q_{tot}$ to each $ Q^{i}$. Based on this theoretical finding , we introduce the multi-head attention mechanism to approximate each term in the decomposing formula with theoretical explanations . Experiments show that our method outperforms state-of-the-art MARL methods on the widely adopted StarCraft benchmarks across different scenarios , and attention analysis is also investigated with sights .
Based on the definitions , we propose a novel Transformer based model for predicting study session dropout , DAS : Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment . DAS has an encoder-decoder structure which is composed of stacked multi-head attention and point-wise feed-forward networks . The deep attentive computations in DAS are capable of capturing complex relations among dynamic student interactions .
However , most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment , as well as the utilization of emojis . To this end , we propose the emotion-semantic-enhanced bidirectional long short-term memory ( BiLSTM ) network with the multi-head attention mechanism model ( EBILSTM-MH ) for sentiment analysis . This model uses BiLSTM to learn feature representation of input texts , given the word embedding .
We test and verify the proposed attention method on three language modeling tasks ( i.e. , PTB , WikiText-103 and One-billion ) and a neural machine translation task ( i.e. , WMT-2016 English-German ) . Multi-linear attention can not only largely compress the model parameters but also obtain performance improvements , compared with a number of language modeling approaches , such as Transformer , Transformer-XL , and Transformer with tensor train decomposition .
One Write-Head is All You Need . Multi-head attention layers , as used in the Transformer neural sequence model , are a powerful alternative to RNNs for moving information across and between sequences . While training these layers is generally fast and simple , due to parallelizability across the length of the sequence , incremental inference ( where such paralleization is impossible ) is often slow , due to the memory-bandwidth cost of repeatedly loading the large " keys " and " values " tensors .
In this work , we investigate the effect of utilizing information not only from the final layer but also from intermediate layers of a pre-trained language representation model to detect grammatical errors . We propose a multi-head multi-layer attention model that determines the appropriate layers in Bidirectional Encoder Representation from Transformers ( BERT ) . The proposed method achieved the best scores on three datasets for grammatical error detection tasks , outperforming the current state-of-the-art method by 6.0 points on FCE , 8.2 points on CoNLL14 , and 12.2 points on JFLEG in terms of F_0.5 .
Besides improving performance , an advantage of using attention is that it can also help to interpret a model by showing how the model assigns weight to different input elements . However , the multi-layer , multi-head attention mechanism in the Transformer model can be difficult to decipher . To make the model more accessible , we introduce an open-source tool that visualizes attention at multiple scales , each of which provides a unique perspective on the attention mechanism .
paragraph : Positional attention We also include an additional positional attention module in each decoder layer , which is a multi - head attention module with the same general attention mechanism used in other parts of the Transformer network , i.e. where is the model hidden size , but with the positional encoding as both query and key and the decoder states as the value . This incorporates positional information directly into the attention process and provides a stronger positional signal than the embedding layer alone .
In this study , DeLight is interfaced with a heart rate variability biofeedback system with two modes for different purposes : stress intervention and relaxation assistance . We evaluated the prototype of DeLight in two user studies . The results of the first study show that DeLight has the potential for stress intervention ; the HRV biofeedback through the changes of ambient light could improve a user ’s awareness of stress and trigger behavioral conditioning , such as deep breathing .
Physical viability , such as timing , memory , or energy efficiency , are standing challenges in realizing the true potential of DNNs . We propose DeLight , a set of novel methodologies which aim to bring physical constraints as design parameters in the training and execution of DNN architectures . We use physical profiling to bound the network size in accordance to the pertinent platform 's characteristics .
Improving Human Pose Estimation With Self-Attention Generative Adversarial Networks Effective Attention Modeling for Aspect-Level Sentiment Classification A Semantic Matching Model Based on Optimal Selection Mechanism The neural mechanism of counting: Selective modulation of activations in the premotor cortex and the intraparietal sulcus by the number of sequential operations Single Shot Attention-Based Face Detector Exploiting Deeply Supervised Inception Networks for Automatically Detecting Traffic Congestion on Freeway in China Using Ultra-Low Frame Rate Videos An attentive self-organizing neural model for text mining Neural Machine Translation of Rare Words with Subword Units Attending to Characters in Neural Sequence Labeling Models Multimodal Residual Learning for Visual QA Towards Conceptual Compression Locality-Aware Parallel Process Mapping for Multi-core HPC Systems Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease Multicasting for all-optical multifiber networks A Planning Approach to the Recognition of Multiple Goals Multiple-goal recognition based on planning Improving Plan Quality through Heuristics for Guiding and Pruning the Search: A Study Using LAMA Optimizing Locality-Aware Memory Management of Key-Value Caches LAMA: Optimized Locality-aware Memory Allocation for Key-value Cache Scheduling in Ad-Hoc Networks Decentralized equalization for massive MU-MIMO on FPGA LAMA: A linguistic aggregation of majority additive operator Ranking der dualen Bronchodilatation steigt Towards a Cognitive Vision Platform for Semantic Image Interpretation; Application to the Recognition of Biological Organisms Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis A Tensorized Transformer for Language Modeling Fast Transformer Decoding: One Write-Head is All You Need Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection A Multiscale Visualization of Attention in the Transformer Model Non-Autoregressive Neural Machine Translation DeLight: biofeedback through ambient light for stress intervention and relaxation assistance Going deeper than deep learning for massive data analytics under physical constraints
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COLING CSAI CCBR ACL COLING NIPS NIPS CLUSTER FSKD SOCS USENIX Annual Technical Conference ACSSC ACL (3) ICLR CODES+ISSS
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