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检索条件"机构=Science and Technology on Parallel and Distributed Laboratory College of Computer"
666 条 记 录,以下是321-330 订阅
Towards a multi-array architecture for accelerating large-scale matrix multiplication on FPGAs
arXiv
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arXiv 2018年
作者: Shen, Junzhong Qiao, Yuran Huang, You Wen, Mei Zhang, Chunyuan College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China
Large-scale floating-point matrix multiplication is a fundamental kernel in many scientific and engineering applications. Most existing work only focus on accelerating matrix multiplication on FPGA by adopting a linea... 详细信息
来源: 评论
Sample dropout for audio scene classification using multi-scale dense connected convolutional neural network
arXiv
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arXiv 2018年
作者: Feng, Dawei Xu, Kele Mi, Haibo Liao, Feifan Zhou, Yan Science and Technology on Parallel and Distributed Laboratory School of Computer National University of Defense Technology Changsha410073 China School of Information and Communication National University of Defense Technology Wuhan430010 China
Acoustic scene classification is an intricate problem for a machine. As an emerging field of research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this paper, we explore the use of multi-sc... 详细信息
来源: 评论
Efficient detection of dangling pointer error for C/C++ programs  2
Efficient detection of dangling pointer error for C/C++ prog...
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2nd Annual International Conference on Information System and Artificial Intelligence, ISAI 2017
作者: Zhang, Wenzhe Science and Technology on Parallel and Distributed Laboratory State Key Laboratory of High Performance Computing State Key Laboratory of High-end Server and Storage Technology College of Computer National University of Defense Technology Changsha China
Dangling pointer error is pervasive in C/C++ programs and it is very hard to detect. This paper introduces an efficient detector to detect dangling pointer error in C/C++ programs. By selectively leave some memory acc... 详细信息
来源: 评论
Loss rank mining: A general hard example mining method for real-time Detectors
arXiv
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arXiv 2018年
作者: Yu, Hao Zhang, Zhaoning Qin, Zheng Wu, Hao Li, Dongsheng Zhao, Jun Lu, Xicheng Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Electronic and Engineering National University of Defense Technology Changsha China College of Meteorology and Oceanology National University of Defense Technology Changsha China
Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detector... 详细信息
来源: 评论
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
Loss Rank Mining: A General Hard Example Mining Method for R...
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International Joint Conference on Neural Networks
作者: Hao Yu Zhaoning Zhang Zheng Qin Hao Wu Dongsheng Li Jun Zhao Xicheng Lu Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Electronic and Engineering National University of Defense Technology Changsha China College of Meteorology and Oceanology National University of Defense Technology Changsha China
Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of back-grounds and become hard examples during training. Compared with those proposal-based ones, real-time detecto... 详细信息
来源: 评论
Collaborative deep learning across multiple data centers
arXiv
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arXiv 2018年
作者: Xu, Kele Mi, Haibo Feng, Dawei Wang, Huaimin Chen, Chuan Zheng, Zibin Lan, Xu National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China School of Data and Computer Science Sun Yat-Sen University Guangzhou China Queen Mary University of London London United Kingdom
Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practi... 详细信息
来源: 评论
Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation
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Archives of Computational Methods in Engineering 2021年 第7期28卷 5039-5039页
作者: Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany
来源: 评论
Fine-grained checkpoint based on non-volatile memory
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Frontiers of Information technology & Electronic Engineering 2017年 第2期18卷 220-234页
作者: Wen-zhe ZHANG Kai LU Mikel LUJAN Xiao-ping WANG Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha 410072 China School of Computer The University of Manchester Manchester M13 9PL UK
New non-volatile memory (e.g., phase-change memory) provides fast access, large capacity, byteaddressability, and non-volatility features. These features, fast-byte-persistency, will bring new opportunities to fault... 详细信息
来源: 评论
Determinants of pull-based development in the context of continuous integration
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science China(Information sciences) 2016年 第8期59卷 53-66页
作者: Yue YU Gang YIN Tao WANG Cheng YANG Huaimin WANG College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing
The pull-based development model, widely used in distributed software teams on open source communities, can efficiently gather the wisdom from crowds. Instead of sharing access to a central repository,contributors cre... 详细信息
来源: 评论
Crowd intelligence in AI 2.0 era
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Frontiers of Information technology & Electronic Engineering 2017年 第1期18卷 15-43页
作者: Wei LI Wen-jun WU Huai-min WANG Xue-qi CHENG Hua-jun CHEN Zhi-hua ZHOU Rong DING State Key Laboratory of Software Development Beihang University National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University National Key Laboratory for Novel Software Technology Nanjing University
The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI... 详细信息
来源: 评论