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检索条件"机构=CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology"
951 条 记 录,以下是31-40 订阅
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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness
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IEEE Transactions on network and Service Management 2025年 第3期22卷 2838-2852页
作者: Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China
With the proliferation of cloud services and the continuous growth in enterprises’ demand for dynamic multidimensional resources, the implementation of effective strategy for time-varying workload scheduling has beco... 详细信息
来源: 评论
SDGNN: Symmetry-Preserving Dual-Stream Graph Neural networks
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IEEE/CAA Journal of Automatica Sinica 2024年 第7期11卷 1717-1719页
作者: Jiufang Chen Ye Yuan Xin Luo the College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China IEEE
Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) ar... 详细信息
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Extending generalized unsupervised manifold alignment
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science China(Information sciences) 2022年 第7期65卷 139-156页
作者: Xiaoyi YIN Zhen CUI Hong CHANG Bingpeng MA Shiguang SHAN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology CAS Center for Excellence in Brain Science and Intelligence Technology
Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with c... 详细信息
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Adversarial Learning data Augmentation for Graph Contrastive Learning in Recommendation  28th
Adversarial Learning Data Augmentation for Graph Contrastiv...
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28th International Conference on database Systems for Advanced Applications, DASFAA 2023
作者: Huang, Junjie Cao, Qi Xie, Ruobing Zhang, Shaoliang Xia, Feng Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China
Recently, Graph Neural networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performa... 详细信息
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Knowledge-Enhanced Self-Supervised Prototypical network for Few-Shot Event Detection
Knowledge-Enhanced Self-Supervised Prototypical Network for ...
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2022 Findings of the Association for Computational Linguistics: EMNLP 2022
作者: Zhao, Kailin Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences China
Prototypical network based joint methods have attracted much attention in few-shot event detection, which carry out event detection in a unified sequence tagging framework. However, these methods suffer from the inacc... 详细信息
来源: 评论
An Extractive Automatic Summarization Method for Chinese Long Text  9th
An Extractive Automatic Summarization Method for Chinese Lon...
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19th International Conference on Advanced data Mining and Applications, ADMA 2023
作者: Zhu, Jizhao Duan, Wenyu Yu, Naitong Pan, Xinlong Fan, Chunlong Shenyang Aerospace University Shenyang China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China Naval Aviation University Yantai China
The extractive automatic summarization method is capable of quickly and efficiently generating summaries through the steps of scoring, extracting and eliminating redundant sentences. Currently, most extractive methods... 详细信息
来源: 评论
What makes a successful rebuttal in computer science conferences?: A perspective on social interaction
What makes a successful rebuttal in computer science confere...
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作者: Huang, Junjie Huang, Win-bin Bu, Yi Cao, Qi Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Information Management Peking University Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China
With an exponential increase in submissionsto top-tier Computer science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review process. The rebuttal stage can be mod... 详细信息
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Sensing–Communication–Computation Integration for Federated Edge Learning With Controllable Model Dropout
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IEEE Internet of Things Journal 2025年 第12期12卷 19767-19781页
作者: Jiao, Xiang Zhu, Guangxu Jiang, Wei Chen, Li Luo, Wu Wen, Dingzhu Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Shenzhen Research Institute of Big Data Shenzhen International Center For Industrial and Applied Mathematics Shenzhen518172 China University of Science and Technology of China CAS Key Laboratory of Wireless Optical Communication Hefei230052 China ShanghaiTech University Network Intelligence Center School of Information Science and Technology Shanghai201210 China
Federated edge learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server... 详细信息
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Caching Optimization in User-Centric networks: A Stochastic Geometry Perspective
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IEEE Transactions on Vehicular technology 2025年 第5期74卷 8187-8200页
作者: Zhang, Chenwu Lu, Hancheng Chen, Chang Wen Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications Hefei230027 China The Hong Kong Polytechnic University Department of Computing Hong Kong
User-centric network (UCN) is regarded as a promising technology to provide users with high network capacity through a cluster of cooperative base stations (BSs). However, to support many-to-one transmission in UCN, t... 详细信息
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Differentially Private Federated Stochastic Primal-Dual Learning for Internet of Vehicles
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IEEE Internet of Things Journal 2025年 第11期12卷 17034-17050页
作者: Li, Yiwei Wang, Shuai Chang, Tsung-Hui Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Chinese University of Hon g Kong School of Science and Engineering Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China
Federated learning (FL) has the potential to empower Internet of Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider ... 详细信息
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