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一种同伴知识互增强下的序列推荐方法 期刊论文
自动化学报, 2023, 卷号: 49, 期号: 7, 页码: 1456-1470
作者:  胡开喜;  李琳;  吴小华;  解庆;  袁景凌
Adobe PDF(1990Kb)  |  收藏  |  浏览/下载：57/20  |  提交时间：2024/04/25
序列推荐  动态兴趣  知识蒸馏  刻意训练  
Machine Learning Methods in Solving the Boolean Satisfiability Problem 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 5, 页码: 640-655
作者:  Wenxuan Guo;  Hui-Ling Zhen;  Xijun Li;  Wanqian Luo;  Mingxuan Yuan;  Yaohui Jin;  Junchi Yan
Adobe PDF(1518Kb)  |  收藏  |  浏览/下载：73/26  |  提交时间：2024/04/23
Machine learning (ML), Boolean satisfiability (SAT), deep learning, graph neural networks (GNNs), combinatorial optimization  
Federated Learning on Multimodal Data: A Comprehensive Survey 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 539-553
作者:  Yi-Ming Lin;   Yuan Gao;  Mao-Guo Gong;  Si-Jia Zhang;  Yuan-Qiao Zhang;  Zhi-Yuan Li
Adobe PDF(1253Kb)  |  收藏  |  浏览/下载：28/10  |  提交时间：2024/04/23
Federated learning, multimodal learning, heterogeneous data, edge computing, collaborative learning  
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 483-513
作者:  Wei-Chien Wang;  Euijoon Ahn;  Dagan Feng;  Jinman Kim
Adobe PDF(2691Kb)  |  收藏  |  浏览/下载：63/19  |  提交时间：2024/04/23
Self-supervised learning (SSL), contrastive learning, deep learning, medical image analysis, computer vision  
A Survey on Collaborative DNN Inference for Edge Intelligence 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 3, 页码: 370-395
作者:  Wei-Qing Ren;  Yu-Ben Qu;  Chao Dong;  Yu-Qian Jing;  Hao Sun;  Qi-Hui Wu;  Song Guo
Adobe PDF(2969Kb)  |  收藏  |  浏览/下载：55/16  |  提交时间：2024/04/23
Artificial intelligence (AI), edge intelligence (EI), distributed computing, deep neural network (DNN), collaborative inference  
Federated Learning with Privacy-preserving and Model IP-right-protection 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 1, 页码: 19-37
作者:  Qiang Yang;  Anbu Huang;  Lixin Fan;  Chee Seng Chan;  Jian Han Lim;  Kam Woh Ng;  Ding Sheng Ong;  Bowen Li
Adobe PDF(2634Kb)  |  收藏  |  浏览/下载：35/9  |  提交时间：2024/04/23
Federated learning  privacy-preserving machine learning  security  decentralized learning  intellectual property protection  
How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 9, 页码: 1781-1796
作者:  Hongyi Lin;  Yang Liu;  Shen Li;  Xiaobo Qu
Adobe PDF(1497Kb)  |  收藏  |  浏览/下载：232/140  |  提交时间：2023/08/10
Autonomous driving  generative adversarial network (GAN)  intelligent transportation system (ITS)  traffic anomaly inspection  traffic flow  
Input Structure Design for Structural Controllability of Complex Networks 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 7, 页码: 1571-1581
作者:  Lifu Wang;  Zhaofei Li;  Guotao Zhao;  Ge Guo;  Zhi Kong
Adobe PDF(2572Kb)  |  收藏  |  浏览/下载：106/40  |  提交时间：2023/06/14
Complex network  input configuration  minimum controlled node set  structural controllability  
Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 6, 页码: 1361-1387
作者:  Sibo Cheng;  César Quilodrán-Casas;  Said Ouala;  Alban Farchi;  Che Liu;  Pierre Tandeo;  Ronan Fablet;  Didier Lucor;  Bertrand Iooss;  Julien Brajard;  Dunhui Xiao;  Tijana Janjic;  Weiping Ding;  Yike Guo;  Alberto Carrassi;  Marc Bocquet;  Rossella Arcucci
Adobe PDF(17725Kb)  |  收藏  |  浏览/下载：124/33  |  提交时间：2023/05/29
Data assimilation (DA)  deep learning  machine learning (ML)  reduced-order-modelling  uncertainty quantification (UQ)  
Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 5, 页码: 1105-1121
作者:  Yue Zhou;  Xin Luo;  MengChu Zhou
Adobe PDF(1723Kb)  |  收藏  |  浏览/下载：161/71  |  提交时间：2023/04/26
Big data analysis.  cryptocurrency transaction network embedding (CTNE)  dynamic network  network embedding  network representation  static network