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检索条件"机构=Zhejiang Key Laboratory of Big Data Intelligent Computing"
743 条 记 录,以下是111-120 订阅
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Prediction of remaining useful life of lithium batteries based on Decayable-LSTM
Prediction of remaining useful life of lithium batteries bas...
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Chinese Control and Decision Conference, CCDC
作者: Penghua Li Dailin Gao Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China
data-driven techniques have been extensively employed in practical applications involving lithium-ion batteries. However, the accuracy of these methods heavily relies on the quality and quantity of the collected data.... 详细信息
来源: 评论
Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse data
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IEEE/CAA Journal of Automatica Sinica 2021年 第4期8卷 796-805页
作者: Di Wu Xin Luo the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China
High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor *** they contain rich information,how to accurately represe... 详细信息
来源: 评论
Learning a Mini-Batch Graph Transformer via Two-Stage Interaction Augmentation  27
Learning a Mini-Batch Graph Transformer via Two-Stage Intera...
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27th European Conference on Artificial Intelligence, ECAI 2024
作者: Li, Wenda Chen, Kaixuan Liu, Shunyu Zheng, Tongya Huang, Wenjie Song, Mingli State Key Laboratory of Blockchain and Security Zhejiang University China School of Software Technology Zhejiang University China Institute of Blockchain and Data Security China Big Graph Center School of Computer and Computing Science Hangzhou City University China College of Computer Science and Technology Zhejiang University Hangzhou China
Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robus... 详细信息
来源: 评论
Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism
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Geophysics 2025年 第3期90卷 WA125-WA140页
作者: Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China
Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation... 详细信息
来源: 评论
TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering  38
TFGDA: Exploring Topology and Feature Alignment in Semi-supe...
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38th Conference on Neural Information Processing Systems, NeurIPS 2024
作者: Dan, Jun Liu, Weiming Xie, Chunfeng Yu, Hua Dong, Shunjie Tan, Yanchao Zhejiang University China Queen Mary University of London United Kingdom Dalian University of Technology China Shanghai Jiao Tong University China Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China Fujian Key Laboratory of Network Computing and Intelligent Information Processing China
Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, mos...
来源: 评论
Research on WNN Greenhouse Temperature Prediction Method Based on GA
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Phyton-International Journal of Experimental Botany 2022年 第10期91卷 2283-2296页
作者: Wenbin Dai Lina Wang Binrui Wang Xiaohong Cui Xue Li College of Mechanical and Electronic Engineering China Jiliang UniversityHangzhou310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou310018China
Temperature in agricultural production has a direct impact on the growth of *** emergence of greenhouses has improved the impact of the original unpredictable changes in temperature,but the temperature modeling of g... 详细信息
来源: 评论
A Model robustness optimization method based on adversarial sample detection  5
A Model robustness optimization method based on adversarial ...
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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022
作者: Sun, Jiaze Long, Siyuan Ma, Xianyan Tang, Yanmei Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Xi'an University of Posts and Telecommunications Xi'an710121 China
Deep neural networks are extremely vulnerable due to the existence of adversarial samples. It is a challenging problem to optimize the robustness of the model to protect deep neural networks from the threat of adversa... 详细信息
来源: 评论
GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators
GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...
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IEEE Symposium on High-Performance Computer Architecture
作者: Siling Yang Shuibing He Wenjiong Wang Yanlong Yin Tong Wu Weijian Chen Xuechen Zhang Xian-He Sun Dan Feng The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Lab Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver Illinois Institute of Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics
Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capabi... 详细信息
来源: 评论
Research on key technologies of edge cache in virtual data space across WAN
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Frontiers of Computer Science 2023年 第1期17卷 15-34页
作者: Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China
The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputi... 详细信息
来源: 评论
How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training
arXiv
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arXiv 2025年
作者: Ou, Yixin Yao, Yunzhi Zhang, Ningyu Jin, Hui Sun, Jiacheng Deng, Shumin Li, Zhenguo Chen, Huajun Zhejiang University China Huawei Noah’s Ark Lab Canada National University of Singapore NUS-NCS Joint Lab Singapore Zhejiang Key Laboratory of Big Data Intelligent Computing China
Despite exceptional capabilities in knowledge-intensive tasks, Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge, particularly how to structurally embed acquired know... 详细信息
来源: 评论