Zobrazeno 1 - 10
of 336
pro vyhledávání: '"SHI Yixin"'
Autor:
SHI Yixin, WANG Yuekun, XING Hao, CHEN Wenlin, LIU Delin, ZHAO Binghao, YANG Tianrui, NIU Pei, WANG Yu, MA Wenbin
Publikováno v:
Xiehe Yixue Zazhi, Vol 14, Iss 2, Pp 306-314 (2023)
Objective The prognosis of patients with brain metastases is poor and lacking in unifieddiagnosis and treatment standards. The demand for multidisciplinary team(MDT) diagnosis and treatment mode is extremely high. This study retrospectively summarize
Externí odkaz:
https://doaj.org/article/3bb1dff9070848f7801aafa1b3981a59
Autor:
CHEN Wenlin, WANG Yaning, XING Hao, LIANG Tingyu, SHI Yixin, WANG Hai, YANG Huiyu, LIU Qianshu, LI Junlin, GUO Xiaopeng, WANG Yu, MA Wenbin
Publikováno v:
Xiehe Yixue Zazhi, Vol 13, Iss 5, Pp 760-767 (2022)
Glioma, the most prevalent primary malignant tumor of the central nervous system, has a high degree of malignancy and poor prognosis for patients. At present, the researches of glioma mainly focus on the investigation of the mechanism of tumor occurr
Externí odkaz:
https://doaj.org/article/8063bd49fa454033915d6207a5751873
Publikováno v:
In Food Chemistry: X 30 October 2024 23
Autor:
Gai, Kuo, Zhang, Tongrui, Xu, Zhengyi, Li, Guangzhao, He, Zihan, Meng, Shuhuai, Shi, Yixin, Zhang, Yuheng, Zhu, Zhou, Pei, Xibo, Wang, Jian, Wan, Qianbing, Cai, He, Li, Yijun, Chen, Junyu
Publikováno v:
In Chemical Engineering Journal 1 August 2024 493
Publikováno v:
In Journal of Affective Disorders 15 June 2024 355:22-30
Autor:
Chen, Tianyi, Ji, Bo, Ding, Tianyu, Fang, Biyi, Wang, Guanyi, Zhu, Zhihui, Liang, Luming, Shi, Yixin, Yi, Sheng, Tu, Xiao
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices. However, the existing pruning methods are usually heuristic, task-specified, and require an extra fine-tuning procedure. To ov
Externí odkaz:
http://arxiv.org/abs/2107.07467
Autor:
Shi, Yixin1 (AUTHOR), He, Xuewen1 (AUTHOR) xheao@suda.edu.cn
Publikováno v:
Molecules. Mar2024, Vol. 29 Issue 5, p983. 18p.
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications. There have been a significant amount of work regarding network compression, whil
Externí odkaz:
http://arxiv.org/abs/2011.04868
Autor:
Chen, Tianyi, Ding, Tianyu, Ji, Bo, Wang, Guanyi, Tian, Jing, Shi, Yixin, Yi, Sheng, Tu, Xiao, Zhu, Zhihui
Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression. In this paper, we present a novel stochastic method -- Orthant Based Proximal Stochastic Gradient Method (
Externí odkaz:
http://arxiv.org/abs/2004.03639
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