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Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation 期刊论文
FRONTIERS IN NEUROROBOTICS, 2020, 卷号: 12, 期号: 无, 页码: 11
作者:  Wang, Jiaxing;  Wang, Weiqun;  Ren, Shixin;  Shi, Weiguo;  Hou, Zeng-Guang
Adobe PDF(2370Kb)  |  收藏  |  浏览/下载：294/69  |  提交时间：2021/01/06
human-in-the-loop optimization  EEG based neural engagement  sEMG based muscle activation  tracking accuracy  neural rehabilitation  
FSA: A Fine-Grained Systolic Accelerator for Sparse CNNs 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2020, 卷号: 39, 期号: 11, 页码: 3589-3600
作者:  Li, Fanrong;  Li, Gang;  Mo, Zitao;  He, Xiangyu;  Cheng, Jian
Adobe PDF(1906Kb)  |  收藏  |  浏览/下载：326/58  |  提交时间：2021/01/06
Accelerator  architecture  convolutional neural networks (CNNs)  sparsity  
Automatic Reconstruction of Mitochondria and Endoplasmic Reticulum in Electron Microscopy Volumes by Deep Learning 期刊论文
FRONTIERS IN NEUROSCIENCE, 2020, 期号: 14, 页码: 13
作者:  Liu, Jing;  Li, Linlin;  Yang, Yang;  Hong, Bei;  Chen, Xi;  Xie, Qiwei;  Han, Hua
浏览  |  Adobe PDF(1944Kb)  |  收藏  |  浏览/下载：284/64  |  提交时间：2020/09/21
mitochondria  endoplasmic reticulum  electron microscopes  segmentation  3D reconstruction  
PolSAR Image Feature Extraction via Co-Regularized Graph Embedding 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 11, 页码: 19
作者:  Huang, Xiayuan;  Nie, Xiangli;  Qiao, Hong
浏览  |  Adobe PDF(8269Kb)  |  收藏  |  浏览/下载：323/67  |  提交时间：2020/08/03
Wishart distance  Euclidean distance of polarimetric features  co-regularized graph embedding  dimensionality reduction  PolSAR image feature extraction  
Bayesian Automatic Model Compression 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2020, 卷号: 14, 期号: 4, 页码: 727-736
作者:  Wang, Jiaxing;  Bai, Haoli;  Wu, Jiaxiang;  Cheng, Jian
Adobe PDF(1918Kb)  |  收藏  |  浏览/下载：276/51  |  提交时间：2020/12/04
Quantization  automatic machine learning  model compression  Bayes methods  Mathematical model  Training  Mixture models  Optimization  Machine learning  Bayesian learning  quantizartion  explainability