Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Li, Dingwei"'
Autor:
Li, Dingwei1,2,3 (AUTHOR), Chen, Yitong2,3 (AUTHOR), Ren, Huihui2,3 (AUTHOR), Tang, Yingjie2,3 (AUTHOR), Zhang, Siyu1,2 (AUTHOR), Wang, Yan2,3 (AUTHOR), Xing, Lixiang1 (AUTHOR), Huang, Qi1 (AUTHOR), Meng, Lei4 (AUTHOR), Zhu, Bowen1,2,5 (AUTHOR) zhubowen@westlake.edu.cn
Publikováno v:
Advanced Science. 10/23/2024, Vol. 11 Issue 39, p1-11. 11p.
Autor:
Zhang, Yingying, Jiang, Zhijiang, Lu, Kaili, Ding, Bingyu, Wang, Jie, Wang, Neili, Li, Dingwei, Yu, Fengnan, Zhang, Mengjiao, Xu, Helin
Publikováno v:
In International Journal of Pharmaceutics 15 November 2024 665
Autor:
Ran, Kunjie, Wang, Jie, Li, Dingwei, Jiang, Zhijiang, Ding, Bingyu, Yu, Fengnan, Hu, Sunkuan, Wang, Lifen, Sun, Wenwen, Xu, Helin
Publikováno v:
In Colloids and Surfaces B: Biointerfaces November 2024 243
Autor:
Yang, Jiaojiao, Wang, Jie, Ding, Bingyu, Jiang, Zhijiang, Yu, Fengnan, Li, Dingwei, Sun, Wenwen, Wang, Lifen, Xu, Helin, Hu, Sunkuan
Publikováno v:
In International Journal of Biological Macromolecules December 2024 282 Part 1
In this paper, we consider recovering $n$ dimensional signals from $m$ binary measurements corrupted by noises and sign flips under the assumption that the target signals have low generative intrinsic dimension, i.e., the target signals can be approx
Externí odkaz:
http://arxiv.org/abs/2111.14486
Recovering sparse signals from observed data is an important topic in signal/imaging processing, statistics and machine learning. Nonconvex penalized least squares have been attracted a lot of attentions since they enjoy nice statistical properties.
Externí odkaz:
http://arxiv.org/abs/2109.08850
Autor:
Jiao, Yuling, Lai, Yanming, Li, Dingwei, Lu, Xiliang, Wang, Fengru, Wang, Yang, Yang, Jerry Zhijian
In recent years, physical informed neural networks (PINNs) have been shown to be a powerful tool for solving PDEs empirically. However, numerical analysis of PINNs is still missing. In this paper, we prove the convergence rate to PINNs for the second
Externí odkaz:
http://arxiv.org/abs/2109.01780
Publikováno v:
In International Journal of Pharmaceutics 15 August 2024 661
Autor:
Shangguan, Jianxun, Yu, Fengnan, Ding, Bingyu, Jiang, Zhijiang, Wang, Jie, Li, Dingwei, Chen, Yi, Zhao, Yingzheng, Hu, Sunkuan, Xu, Helin
Publikováno v:
In Acta Biomaterialia August 2024 184:127-143
Autor:
Li, Dingwei, Chang, Qinglong, Pang, Lixue, Zhang, Yanfang, Sun, Xudong, Ding, Jikun, Zhang, Liang
Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency. In this paper, we propose a high efficient FL method equipped with the
Externí odkaz:
http://arxiv.org/abs/2012.08809