中国科学院计算技术研究所机构知识库

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发表日期：2023

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	DyTSCL: Dynamic graph representation via tempo-structural contrastive learning 期刊论文 NEUROCOMPUTING, 2023, 卷号: 556, 页码: 8 作者:  Li, Jianian;  Bao, Peng;  Yan, Rong;  Shen, Huawei 收藏  |  浏览/下载：16/0  |  提交时间：2023/12/04 Graph representation learning  Contrastive learning  Dynamic graph  Tempo-structural information
	DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism 期刊论文 BMC MEDICAL IMAGING, 2023, 卷号: 23, 期号: 1, 页码: 15 作者:  Wang, Han;  Lei, Cai;  Zhao, Di;  Gao, Liwei;  Gao, Jingyang 收藏  |  浏览/下载：12/0  |  提交时间：2023/12/04 Segmentation of hippocampus  Deep learning  Dense block, Attention, Data augmentation
	Accelerating Deformable Convolution Networks with Dynamic and Irregular Memory Accesses 期刊论文 ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS, 2023, 卷号: 28, 期号: 4, 页码: 23 作者:  Chu, Cheng;  Liu, Cheng;  Xu, Dawen;  Wang, Ying;  Luo, Tao;  Li, Huawei;  Li, Xiaowei 收藏  |  浏览/下载：15/0  |  提交时间：2023/12/04 Deformable convolution network  neural network accelerator  irregular memory access  runtime tile scheduling
	Visual (EC)-C-2: AI-Driven Visual End-Edge-Cloud Architecture for 6G in Low-Carbon Smart Cities 期刊论文 IEEE WIRELESS COMMUNICATIONS, 2023, 卷号: 30, 期号: 3, 页码: 204-210 作者:  Yang, Zheming;  Hu, Dieli;  Guo, Qi;  Zuo, Lulu;  Ji, Wen 收藏  |  浏览/下载：15/0  |  提交时间：2023/12/04 6G mobile communication  Wireless communication  Visualization  Energy consumption  Smart cities  Computer architecture  Carbon dioxide
	Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing 期刊论文 IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2023, 卷号: 42, 期号: 5, 页码: 1518-1531 作者:  Cui, Yangguang;  Cao, Kun;  Zhou, Junlong;  Wei, Tongquan 收藏  |  浏览/下载：13/0  |  提交时间：2023/12/04 Training  Servers  Cloud computing  Delays  Costs  Computational modeling  Prototypes  Device frequency determination  federated learning (FL)  high efficiency  low cost  mobile-edge cloud computing (MECC)  user selection