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N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning 期刊论文
Scientific Data, 2022, 卷号: 9, 期号: 1, 页码: 9
作者:  Li, Yang;  Dong, Yiting;  Zhao, Dongcheng;  Zeng, Yi
Adobe PDF(4359Kb)  |  收藏  |  浏览/下载：166/15  |  提交时间：2023/03/20
BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons 期刊论文
Frontiers in Neuroscience, 2022, 卷号: 16, 页码: 13
作者:  Li, Yang;  Zhao, Dongcheng;  Zeng, Yi
Adobe PDF(3930Kb)  |  收藏  |  浏览/下载：258/6  |  提交时间：2022/11/21
spiking neural network  bistability  neuromorphic computing  image classification  conversion  
Solving the spike feature information vanishing problem in spiking deep Q network with potential based normalization 期刊论文
FRONTIERS IN NEUROSCIENCE, 2022, 卷号: 16, 页码: 11
作者:  Sun, Yinqian;  Zeng, Yi;  Li, Yang
Adobe PDF(1561Kb)  |  收藏  |  浏览/下载：260/38  |  提交时间：2022/11/14
brain-inspired decision model  SDQN  reinforcement learning  potential normalization  spiking activity  
Spiking CapsNet: A spiking neural network with a biologically plausible routing rule between capsules 期刊论文
Information Sciences, 2022, 卷号: 610, 页码: 1-13
作者:  Zhao, Dongcheng;  Li, Yang;  Zeng, Yi;  Wang, Jihang;  Zhang, Qian
Adobe PDF(3233Kb)  |  收藏  |  浏览/下载：212/9  |  提交时间：2022/11/14
Spiking Neural Network  Capsual Neural Netowrk  Biologically Plausible Routing  Noise Robustness  Affine Transformation Robustness  
Geolocation Error Estimation and Correction on Long-Term MWRI Data 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 卷号: 59, 期号: 11, 页码: 9448-9461
作者:  Liu, Jiazheng;  Li, Weifu;  Peng, Jiangtao;  Shen, Lijun;  Han, Hua;  Zhang, Peng;  Yang, Lei
Adobe PDF(8747Kb)  |  收藏  |  浏览/下载：226/9  |  提交时间：2021/12/28
Geology  Sea surface  Satellites  Earth  Instruments  Satellite broadcasting  Microwave radiometry  Geolocation error estimation and correction  Micro-Wave Radiation Imager (MWRI)  nonrigid point set registration  
Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario 期刊论文
RADIOTHERAPY AND ONCOLOGY, 2021, 卷号: 161, 期号: 10.1016/j.radonc.2021.06.024, 页码: 230-240
作者:  Yang, Ruijie;  Yang, Xueying;  Wang, Le;  Li, Dingjie;  Guo, Yuexin;  Li, Ying;  Guan, Yumin;  Wu, Xiangyang;  Xu, Shouping;  Zhang, Shuming;  Chan, Maria F.;  Geng, Lisheng;  Sui, Jing
Adobe PDF(2840Kb)  |  收藏  |  浏览/下载：397/63  |  提交时间：2021/11/02
Machine learning  VMAT patient-specific QA  Multi-institution validation  Commissioning  Clinical implementation