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检索条件"机构=CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology"
907 条 记 录,以下是741-750 订阅
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MQGrad: Reinforcement learning of gradient quantization in parameter server
arXiv
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arXiv 2018年
作者: Cui, Guoxin Xu, Jun Zeng, Wei Lan, Yanyan Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of ComputingTechnology Chinese Academy of Sciences Beijing100190 China
One of the most significant bottleneck in training large scale machine learning models on parameter server (PS) is the communication overhead, because it needs to frequently exchange the model gradients between the wo... 详细信息
来源: 评论
Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning
arXiv
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arXiv 2018年
作者: Humayoo, Mahammad Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China
Off-policy learning is more unstable compared to on-policy learning in reinforcement learning (RL). One of the reasons for instability of off-policy learning is a discrepancy between target (π) and behavior (b) polic... 详细信息
来源: 评论
General-Purpose Quantum Circuit Simulator with Projected Entangled-Pair States and the Quantum Supremacy Frontier
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Physical Review Letters 2019年 第19期123卷 190501-190501页
作者: Chu Guo Yong Liu Min Xiong Shichuan Xue Xiang Fu Anqi Huang Xiaogang Qiang Ping Xu Junhua Liu Shenggen Zheng He-Liang Huang Mingtang Deng Dario Poletti Wan-Su Bao Junjie Wu Henan Key Laboratory of Quantum Information and Cryptography IEU Zhengzhou 450001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China Information Systems Technology and Design Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Quantum Intelligence Lab (QI-Lab) Supremacy Future Technologies (SFT) Guangzhou 511340 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China Science and Math Cluster and EPD Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore
Recent advances on quantum computing hardware have pushed quantum computing to the verge of quantum supremacy. Here, we bring together many-body quantum physics and quantum computing by using a method for strongly int... 详细信息
来源: 评论
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition
arXiv
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arXiv 2020年
作者: Wu, Xun Zheng, Wei-Long Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Clinical Data Animation Center Department of Neurology Massachusetts General Hospital Harvard Medical School 55 Fruit Street BostonMA United States
Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not w... 详细信息
来源: 评论
Robust classification with sparse representation fusion on diverse data subsets
arXiv
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arXiv 2019年
作者: Feng, Chun-Mei Xu, Yong Li, Zuoyong Yang, Jian Bio-Computing Research Center Harbin Institute of Technology Shenzhen Guangdong518055 China Key Laboratory of Network Oriented Intelligent Computation Shenzhen Guangdong518055 China Fuzhou350121 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu210094 China
Sparse Representation (SR) techniques encode the test samples into a sparse linear combination of all training samples and then classify the test samples into the class with the minimum residual. The classification of... 详细信息
来源: 评论
Constraining Cosmological Phase Transitions with the Parkes Pulsar Timing Array
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Physical Review Letters 2021年 第25期127卷 251303-251303页
作者: Xiao Xue Ligong Bian Jing Shu Qiang Yuan Xingjiang Zhu N. D. Ramesh Bhat Shi Dai Yi Feng Boris Goncharov George Hobbs Eric Howard Richard N. Manchester Christopher J. Russell Daniel J. Reardon Ryan M. Shannon Renée Spiewak Nithyanandan Thyagarajan Jingbo Wang CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China II. Institute of Theoretical Physics Universität Hamburg 22761 Hamburg Germany Department of Physics Chongqing University Chongqing 401331 China Chongqing Key Laboratory for Strongly Coupled Physics Chongqing 401331 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou 310024 China Center for High Energy Physics Peking University Beijing 100871 China International Center for Theoretical Physics Asia-Pacific Beijing/Hanzhou China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing 210023 China School of Astronomy and Space Science University of Science and Technology of China Hefei 230026 China School of Physics and Astronomy Monash University Clayton VIC 3800 Australia OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery Hawthorn VIC 3122 Australia Advanced Institute of Natural Sciences Beijing Normal University at Zhuhai 519087 China International Centre for Radio Astronomy Research Curtin University Bentley WA 6102 Australia Western Sydney University Locked Bag 1797 Penrith South DC NSW 1797 Australia University of Chinese Academy of Sciences Beijing 100049 China CSIRO Astronomy and Space Science P.O. Box 76 Epping NSW 1710 Australia Macquarie University Department of Physics and Astronomy Sydney NSW 2109 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 Alexandria NSW 1435 Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 Hawthorn VIC 3122 Australia Jodrell Bank Centre for Astrophysics University of Manchester Manchester M13 9PL United Kingdom CSIRO Astronomy and Space Science
A cosmological first-order phase transition is expected to produce a stochastic gravitational wave background. If the phase transition temperature is on the MeV scale, the power spectrum of the induced stochastic grav... 详细信息
来源: 评论
Dynamic-K recommendation with personalized decision boundary  23rd
Dynamic-K recommendation with personalized decision boundary
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23rd China conference on Information Retrieval, CCIR 2017
作者: Gao, Yan Guo, Jiafeng Lan, Yanyan Liao, Huaming CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China
In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a per... 详细信息
来源: 评论
Beyond precision: A study on recall of initial retrieval with neural representations
arXiv
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arXiv 2018年
作者: Xiao, Yan Guo, Jiafeng Fan, Yixing Lan, Yanyan Xu, Jun Cheng, Xueqi University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China
Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in... 详细信息
来源: 评论
Fully-convolutional intensive feature flow neural network for text recognition
arXiv
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arXiv 2019年
作者: Zhang, Zhao Tang, Zemin Zhang, Zheng Wang, Yang Qin, Jie Wang, Meng School of Computer Science and Technology Soochow University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates
The Deep Convolutional Neural networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the tra... 详细信息
来源: 评论
Coarse to fine: Diffusing categories in wikipedia  26
Coarse to fine: Diffusing categories in wikipedia
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26th International World Wide Web Conference, WWW 2017 Companion
作者: Cai, Pengshan Feng, Yansong Jia, Yantao Wang, Yuanzhuo Jin, Xiaolong Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computer Science and Technology Peking University Beijing China
Automatic taxonomy construction aims to build a categorization system without human efforts. Traditional textual pattern based methods extract hyponymy relation in raw texts. However, these methods usually yield low p... 详细信息
来源: 评论