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A Reinforcement Learning Based Approach to Partition Testing
《Journal of Computer Science & Technology》2025年第1期99-118,共20页Chang-Ai Sun Ming-Jun Xiao He-Peng Dai Huai Liu 
supported by the National Natural Science Foundation of China under Grant Nos.62272037 and 61872039;the Beijing Natural Science Foundation under Grant No.4162040;the Aeronautical Science Foundation of China under Grant No.2016ZD74004;the Fundamental Research Funds for the Central Universities of China under Grant No.FRF-GF-19-B19;the Australian Research Council Discovery Project under Grant No.DP210102447.
Partition testing is one of the most fundamental and popularly used software testing techniques.It first divides the input domain of the program under test into a set of disjoint partitions,and then creates test cases...
关键词：partition testing reinforcement learning temporal-difference learning intelligent software engineering 
Data-driven comparison of federated learning and model personalization for electric load forecasting
《Energy and AI》2023年第4期3-16,共14页Fabian Widmer Severin Nowak Benjamin Bowler Patrick Huber Antonios Papaemmanouil 
Residential short-term electric load forecasting is essential in modern decentralized power systems.Load forecasting methods mostly rely on neural networks and require access to private and sensitive electric load dat...
关键词：Federated learning Machine learning Model personalization Temporal convolutional network Electric load forecast Differential comparison 
Rolling Shutter Camera:Modeling,Optimization and Learning
《Machine Intelligence Research》2023年第6期783-798,共16页Bin Fan Yuchao Dai Mingyi He 
This work was supported in part by National Natural Science Foundation of China(Nos.62271410,61901387 and 62001394);the Fundamental Research Funds for the Central Universities,China,and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX2022046).
Most modern consumer-grade cameras are often equipped with a rolling shutter mechanism,which is becoming increasingly important in computer vision,robotics and autonomous driving applications.However,its temporal-dyna...
关键词：Rolling shutter motion modeling image correction temporal super-resolution deep learning 
Supervised Contrastive Learning with Term Weighting for Improving Chinese Text Classification
《Tsinghua Science and Technology》2023年第1期59-68,共10页Jiabao Guo Bo Zhao Hui Liu Yifan Liu Qian Zhong 
supported by the National Natural Science Foundation of China (No.U1936122);Primary Research&Developement Plan of Hubei Province (Nos.2020BAB101 and 2020BAA003).
With the rapid growth of information retrieval technology,Chinese text classification,which is the basis of information content security,has become a widely discussed topic.In view of the huge difference compared with...
关键词：Chinese text classification Supervised Contrastive Learning(SCL) Term Weighting(TW) Temporal Convolution Network(TCN) 
A geospatial service composition approach based on MCTS with temporal-difference learning
《High Technology Letters》2021年第1期17-25,共9页Zhuang Can Guo Mingqiang Xie Zhong 
Supported by the National Natural Science Foundation of China(No.41971356,41671400,41701446);National Key Research and Development Program of China(No.2017YFB0503600,2018YFB0505500);Hubei Province Natural Science Foundation of China(No.2017CFB277)。
With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is ri...
关键词：geospatial service composition reinforcement learning(RL) Markov decision process(MDP) Monte Carlo tree search(MCTS) temporal-difference(TD)learning 
Distributed multi-agent temporal-difference learning with full neighbor information
《Control Theory and Technology》2020年第4期379-389,共11页Zhinan Peng Jiangping Hu Rui Luo Bijoy K.Ghosh 
the Sichuan Science and Technology Program(No.2020YFSY0012);the National Natural Science Foundation of China(Nos.61473061,61104104);the Program for New Century Excellent Talents in University(No.NCET-13-0091).
This paper presents a novel distributed multi-agent temporal-difference learning framework for value function approximation,which alows agents using all the neighbor information instead of the information from only on...
关键词：Distributed algorithm Reinforcement learning Temprel-lifferene learning Multi-agent systems 
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