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Distributed stochastic gradient tracking methods with momentum acceleration for non-convex optimization 期刊论文
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2022, 页码: 42
作者:  Gao, Juan;  Liu, Xin-Wei;  Dai, Yu-Hong;  Huang, Yakui;  Gu, Junhua
收藏  |  浏览/下载：96/0  |  提交时间：2023/02/07
Distributed non-convex optimization  Machine learning  Momentum methods  Optimization algorithms  Convergence rate  
Monte Carlo fPINNs: Deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations 期刊论文
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2022, 卷号: 400, 页码: 17
作者:  Guo, Ling;  Wu, Hao;  Yu, Xiaochen;  Zhou, Tao
收藏  |  浏览/下载：51/0  |  提交时间：2023/02/07
Physics -informed neural networks  Fractional Laplacian  Nonlocal operators  Uncertainty quantification  

Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models

 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 卷号: 461, 页码: 18
作者:  Guo, Ling;  Wu, Hao;  Zhou, Tao
收藏  |  浏览/下载：126/0  |  提交时间：2023/02/07
Data -driven modeling  Normalizing flows  Uncertainty quantification  Random fields  
Prediction of the transcription factor binding sites with meta-learning 期刊论文
METHODS, 2022, 卷号: 203, 页码: 207-213
作者:  Jing, Fang;  Zhang, Shao-Wu;  Zhang, Shihua
收藏  |  浏览/下载：173/0  |  提交时间：2023/02/07
Convolution neural network  Transcription factor binding sites  Meta learning  Noisy labels data  
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder 期刊论文
NATURE COMMUNICATIONS, 2022, 卷号: 13, 期号: 1, 页码: 12
作者:  Dong, Kangning;  Zhang, Shihua
收藏  |  浏览/下载：102/0  |  提交时间：2022/06/21
Disease category-specific annotation of variants using an ensemble learning framework 期刊论文
BRIEFINGS IN BIOINFORMATICS, 2022, 卷号: 23, 期号: 1, 页码: 15
作者:  Cao, Zhen;  Huang, Yanting;  Duan, Ran;  Jin, Peng;  Qin, Zhaohui S.;  Zhang, Shihua
收藏  |  浏览/下载：119/0  |  提交时间：2022/06/21
complex disease  disease category  functional annotation  non-coding variant  ensemble learning