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Hierarchical compositional representations for few-shot action recognition 期刊论文
COMPUTER VISION AND IMAGE UNDERSTANDING, 2024, 卷号: 240, 页码: 11
作者:  Li, Changzhen;  Zhang, Jie;  Wu, Shuzhe;  Jin, Xin;  Shan, Shiguang
收藏  |  浏览/下载：4/0  |  提交时间：2024/05/20
Action recognition  Few-shot learning  Hierarchical compositional representations  Body parts  EMD distance  
Self-supervised noise modeling and sparsity guided electron tomography volumetric image denoising 期刊论文
ULTRAMICROSCOPY, 2024, 卷号: 255, 页码: 11
作者:  Yang, Zhidong;  Zang, Dawei;  Li, Hongjia;  Zhang, Zhao;  Zhang, Fa;  Han, Renmin
收藏  |  浏览/下载：2/0  |  提交时间：2024/05/20
Electron tomography  Image denoising  Self-supervised learning  
Revisiting AUC-Oriented Adversarial Training With Loss-Agnostic Perturbations 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 12, 页码: 15494-15511
作者:  Yang, Zhiyong;  Xu, Qianqian;  Hou, Wenzheng;  Bao, Shilong;  He, Yuan;  Cao, Xiaochun;  Huang, Qingming
收藏  |  浏览/下载：4/0  |  提交时间：2024/05/20
Optimization  Training  Perturbation methods  Machine learning  Receivers  Machine learning algorithms  Linear programming  AUC Optimization  adversarial learning  machine learning  
AUC-Oriented Domain Adaptation: From Theory to Algorithm 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 12, 页码: 14161-14174
作者:  Yang, Zhiyong;  Xu, Qianqian;  Bao, Shilong;  Wen, Peisong;  He, Yuan;  Cao, Xiaochun;  Huang, Qingming
收藏  |  浏览/下载：3/0  |  提交时间：2024/05/20
AUC-oriented Learning  domain adaptation  machine learning  
Hardware Acceleration for SLAM in Mobile Systems 期刊论文
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2023, 卷号: 38, 期号: 6, 页码: 1300-1322
作者:  Fan, Zhe;  Hao, Yi-Fan;  Zhi, Tian;  Guo, Qi;  Du, Zi-Dong
收藏  |  浏览/下载：2/0  |  提交时间：2024/05/20
hardware accelerator  instruction set  mobile system  simultaneous localization and mapping (SLAM) algorithm  
Positive-Unlabeled Learning With Label Distribution Alignment 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 12, 页码: 15345-15363
作者:  Jiang, Yangbangyan;  Xu, Qianqian;  Zhao, Yunrui;  Yang, Zhiyong;  Wen, Peisong;  Cao, Xiaochun;  Huang, Qingming
收藏  |  浏览/下载：2/0  |  提交时间：2024/05/20
Estimation  Stochastic processes  Optimization  Computer science  Predictive models  Information processing  Fasteners  Positive-unlabeled learning  weakly supervised learning  binary classification  
FeCrypto: Instruction Set Architecture for Cryptographic Algorithms Based on FeFET-Based In-Memory Computing 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2023, 卷号: 42, 期号: 9, 页码: 2889-2902
作者:  Liu, Rui;  Zhang, Xiaoyu;  Xie, Zhiwen;  Wang, Xinyu;  Li, Zerun;  Chen, Xiaoming;  Han, Yinhe;  Tang, Minghua
收藏  |  浏览/下载：9/0  |  提交时间：2023/12/04
Computing-in-memory (CiM)  cryptographic algorithm  ferroelectric field-effect transistor (FeFET)  instruc-tion set architecture (ISA)  
Optimizing Two-Way Partial AUC With an End-to-End Framework 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 8, 页码: 10228-10246
作者:  Yang, Zhiyong;  Xu, Qianqian;  Bao, Shilong;  He, Yuan;  Cao, Xiaochun;  Huang, Qingming
收藏  |  浏览/下载：7/0  |  提交时间：2023/12/04
AUC Optimization  machine learning  partial AUC  
DyPipe: A Holistic Approach to Accelerating Dynamic Neural Networks with Dynamic Pipelining 期刊论文
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2023, 卷号: 38, 期号: 4, 页码: 899-910
作者:  Zhuang, Yi-Min;  Hu, Xing;  Chen, Xiao-Bing;  Zhi, Tian
收藏  |  浏览/下载：2/0  |  提交时间：2024/05/20
dynamic neural network (NN)  deep neural network (DNN) accelerator  dynamic pipelining  
Rethinking Label Flipping Attack: From Sample Masking to Sample Thresholding 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 6, 页码: 7668-7685
作者:  Xu, Qianqian;  Yang, Zhiyong;  Zhao, Yunrui;  Cao, Xiaochun;  Huang, Qingming
收藏  |  浏览/下载：7/0  |  提交时间：2023/12/04
Data models  Training data  Training  Deep learning  Predictive models  Testing  Optimization  Label flipping attack  machine learning