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GPR: Grasp Pose Refinement Network for Cluttered Scenes 会议论文
, 中国西安, 2021-5-31
作者:  Wei W(韦伟);  Luo YK(罗永康);  Li FY(李富裕);  Xu GY(许广云);  Zhong J(钟君);  Li WY(黎万义);  Wang P(王鹏)
Adobe PDF(4288Kb)  |  收藏  |  浏览/下载：86/7  |  提交时间：2024/01/31
Single-Image Specular Highlight Removal via Real-World Dataset Construction 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 页码: 12
作者:  Wu ZQ(吴仲琦);  Zhuang CQ(庄传青);  Shi J(石剑);  Guo JW(郭建伟);  Xiao J(肖俊);  Zhang XP(张晓鹏);  Yan DM(严冬明)
Adobe PDF(49307Kb)  |  收藏  |  浏览/下载：252/97  |  提交时间：2022/06/15
Specular highlight removal, PSD-Dataset, Deep learning  
Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 14
作者:  Zhou, Junjie;  Zhong, Shanlin;  Wu, Wei
Adobe PDF(5440Kb)  |  收藏  |  浏览/下载：283/49  |  提交时间：2022/01/27
Brain-inspired decision making  Fitts' law  Motion generation  Musculoskeletal system  Speed-accuracy tradeoff (SAT)  
A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study 期刊论文
EBIOMEDICINE, 2021, 卷号: 70, 页码: 10
作者:  Zhong, Lianzhen;  Dong, Di;  Fang, Xueliang;  Zhang, Fan;  Zhang, Ning;  Zhang, Liwen;  Fang, Mengjie;  Jiang, Wei;  Liang, Shaobo;  Li, Cong;  Liu, Yujia;  Zhao, Xun;  Cao, Runnan;  Shan, Hong;  Hu, Zhenhua;  Ma, Jun;  Tang, Linglong;  Tian, Jie
Adobe PDF(3679Kb)  |  收藏  |  浏览/下载：395/73  |  提交时间：2021/11/03
Multi-task deep learning  Radiomic nomogram  Survival analysis  Treatment decision  Advanced nasopharyngeal carcinoma  
BlockQNN: Efficient Block-Wise Neural Network Architecture Generation 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 卷号: 43, 期号: 7, 页码: 2314-2328
作者:  Zhong, Zhao;  Yang, Zichen;  Deng, Boyang;  Yan, Junjie;  Wu, Wei;  Shao, Jing;  Liu, Cheng-Lin
收藏  |  浏览/下载：263/0  |  提交时间：2021/11/03
Computer architecture  Task analysis  Neural networks  Network architecture  Graphics processing units  Acceleration  Indexes  Convolutional neural network  neural architecture search  AutoML  reinforcement learning  Q-learning  
Non-line-of-Sight Imaging via Neural Transient Fields 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 卷号: 43, 期号: 7, 页码: 2314-2328
作者:  Zhong, Zhao;  Yang, Zichen;  Deng, Boyang;  Yan, Junjie;  Wu, Wei;  Shao, Jing;  Liu, Cheng-Lin
收藏  |  浏览/下载：293/0  |  提交时间：2021/08/15
Transient analysis  Image reconstruction  Imaging  Nonlinear optics  Measurement by laser beam  Surface reconstruction  Solid modeling  Computational photography  non-line-of-sight imaging  neural radiance field  neural rendering  
Developing Neuroimaging Biomarker for Brain Diseases with a Machine Learning Framework and the Brainnetome Atlas 期刊论文
NEUROSCIENCE BULLETIN, 2021, 页码: 3
作者:  Shi, Weiyang;  Fan, Lingzhong;  Jiang, Tianzi
Adobe PDF(155Kb)  |  收藏  |  浏览/下载：317/49  |  提交时间：2021/08/15
Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study 期刊论文
GASTROINTESTINAL ENDOSCOPY, 2021, 卷号: 93, 期号: 6, 页码: 1333-+
作者:  Hu, Hao;  Gong, Lixin;  Dong, Di;  Zhu, Liang;  Wang, Min;  He, Jie;  Shu, Lei;  Cai, Yiling;  Cai, Shilun;  Su, Wei;  Zhong, Yunshi;  Li, Cong;  Zhu, Yongbei;  Fang, Mengjie;  Zhong, Lianzhen;  Yang, Xin;  Zhou, Pinghong;  Tian, Jie
Adobe PDF(2330Kb)  |  收藏  |  浏览/下载：318/63  |  提交时间：2021/08/15
ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2021, 卷号: 8, 期号: 5, 页码: 1015-1024
作者:  Xiurui Hou;  Kai Wang;  Cheng Zhong;  Zhi Wei
Adobe PDF(18247Kb)  |  收藏  |  浏览/下载：174/21  |  提交时间：2021/04/09
Graph convolution network  long-short term memory network  stock market forecasting  variational autoencoder (VAE)  
Sample-based online learning for bi-regular hinge loss 期刊论文
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 页码: 16
作者:  Xue, Wei;  Zhong, Ping;  Zhang, Wensheng;  Yu, Gaohang;  Chen, Yebin
收藏  |  浏览/下载：182/0  |  提交时间：2021/03/08
SVM  Max-margin classification  Hinge loss  Elastic net  Online learning