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检索条件"机构=Key Laboratory of Intelligent Computing and Application"
153 条 记 录,以下是131-140 订阅
排序:
Encoding Global Semantic and Localized Geographic Spatial-Temporal Relations for Traffic Accident Risk Prediction
SSRN
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SSRN 2024年
作者: Alhaek, Fares Li, Tianrui Rajeh, Taha M. Javed, Muhammad Hafeez Liang, Weichao School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China
The proliferation of vehicles and the intricate layout of road systems have contributed to a significant rise in traffic accidents, posing a pressing concern globally. Despite the advancements facilitated by deep lear... 详细信息
来源: 评论
Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare
SSRN
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SSRN 2024年
作者: Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China
Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without... 详细信息
来源: 评论
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach
arXiv
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arXiv 2024年
作者: Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China
Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilit... 详细信息
来源: 评论
A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction
A Diverse Biases Non-negative Latent Factorization of Tensor...
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IEEE International Conference on Networking, Sensing and Control
作者: Xuke Wu Hang Gou Hao Wu Juan Wang Minzhi Chen Siyu Lai Computer School China West Normal University Nanchong China Computer School of China West Normal University Nanchong Sichuan China University of Chinese Academy of Sciences Beijing China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China North Sichuan Medical College Nanchong China Medical College Nanchong Sichuan China
Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effec... 详细信息
来源: 评论
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning
arXiv
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arXiv 2024年
作者: Tang, Hao Lu, Junhao Huang, Guoheng Li, Ming Chen, Xuhang Zhong, Guo Tan, Zhengguang Li, Zinuo School of Computer Science and Technology Guangdong University of Technology Guangdong China School of Computer Science and Engineering Huizhou University Huizhou China Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of Physics Maths and Computing University of Western Australia WA Australia
In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity. However, in natural scenes, the spatial arrangement of key instances is often inconsistent across ima... 详细信息
来源: 评论
Predicting Large-scale Protein-protein Interactions by Extracting Coevolutionary Patterns with MapReduce Paradigm
Predicting Large-scale Protein-protein Interactions by Extra...
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IEEE International Conference on Systems, Man and Cybernetics
作者: Lun Hu Bo-Wei Zhao Shicheng Yang Xin Luo MengChu Zhou Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China School of Computer Science and Technology Wuhan University of Technology Wuhan China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China New Jersey Institute of Technology Newark NJ USA
Protein-protein interactions are of great significance for us to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technology, the amount of protein-protein intera... 详细信息
来源: 评论
Blockchain-based lightweight trusted data interaction scheme for cross-domain IIoT
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Digital Communications and Networks 2024年
作者: Fengqi Li Yingjie Zhao Kaiyang Zhang Hui Xu Yanjuan Wang Deguang Wang School of Railway Intelligent Engineering Dalian Jiaotong University Dalian 116028 China Dalian Key Laboratory of Blockchain Technology and Application Dalian 116028 China Yunnan Key Laboratory of Service Computing Kunming 650221 China
To facilitate cross-domain data interaction in the Industrial Internet of Things (IIoT), establishing trust between multiple administrative domains is essential. Although blockchain technology has been proposed as a s... 详细信息
来源: 评论
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
arXiv
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arXiv 2024年
作者: Huang, Keke Wang, Yu Guang Li, Ming Liò, Pietro School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge United Kingdom
Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. ... 详细信息
来源: 评论
How universal polynomial bases enhance spectral graph neural networks: heterophily, over-smoothing, and over-squashing  24
How universal polynomial bases enhance spectral graph neural...
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Proceedings of the 41st International Conference on Machine Learning
作者: Keke Huang Yu Guang Wang Ming Li Pietro Liò School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China and Shanghai AI Laboratory Shanghai China and School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge UK
Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. ...
来源: 评论
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
arXiv
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arXiv 2021年
作者: Zheng, Yizhen Jin, Ming Pan, Shirui Li, Yuan-Fang Peng, Hao Li, Ming Li, Zhao Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia SouthportQLD4222 Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China Alibaba Group Hangzhou311100 China
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtai... 详细信息
来源: 评论