Zobrazeno 1 - 10
of 218
pro vyhledávání: '"SHI Junyu"'
Publikováno v:
Shanghai Jiaotong Daxue xuebao. Yixue ban, Vol 44, Iss 11, Pp 1433-1438 (2024)
Objective·To assess the accuracy and time of the two-in-one registration technique by comparing it with the U-shaped tube registration in dynamic navigation implantation.Methods·Thirty standardized 3D-printed models with mandibular posterior sites
Externí odkaz:
https://doaj.org/article/9d86b1e203ce49a28e108f20954f20ef
Autor:
Shi, Junyu, Wang, Baoxuan
Human motion prediction based on 3D skeleton is a significant challenge in computer vision, primarily focusing on the effective representation of motion. In this paper, we propose a self-supervised learning framework designed to enhance motion repres
Externí odkaz:
http://arxiv.org/abs/2408.02091
Autor:
Zhang, Yechao, Hu, Shengshan, Zhang, Leo Yu, Shi, Junyu, Li, Minghui, Liu, Xiaogeng, Wan, Wei, Jin, Hai
Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided gu
Externí odkaz:
http://arxiv.org/abs/2307.07873
Autor:
Li, Minghui, Wan, Wei, Lu, Jianrong, Hu, Shengshan, Shi, Junyu, Zhang, Leo Yu, Zhou, Man, Zheng, Yifeng
Federated learning is a newly emerging distributed learning framework that facilitates the collaborative training of a shared global model among distributed participants with their privacy preserved. However, federated learning systems are vulnerable
Externí odkaz:
http://arxiv.org/abs/2210.01437
Autor:
Zhang, Yi a, b, †, Shi, Junyu a, †, Zhu, Jie b, Ding, Xinxin a, Wei, Jianxu a, Jiang, Xue a, Yang, Yijie a, Zhang, Xiaomeng a, ⁎, Huang, Yongzhuo b, c, d, ⁎, Lai, Hongchang a, ⁎
Publikováno v:
In Acta Pharmaceutica Sinica B November 2024 14(11):5026-5036
Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained autochthonous. Compared with the traditional machine learning framewo
Externí odkaz:
http://arxiv.org/abs/2112.14468
Publikováno v:
Anti-Corrosion Methods and Materials, 2023, Vol. 70, Issue 6, pp. 438-448.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ACMM-06-2023-2840
Akademický článek
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Autor:
Jiao, Mengya a, Sun, Yan f, g, Shi, Junyu h, Zhang, Na a, Tang, Xuhuan a, Fan, Anqi i, Liu, Shiwang a, Dai, Chan a, Qian, Zhigang a, Zhang, Feng a, Wang, Chenchen e, ⁎, Chen, Huoying c, d, ⁎, Zheng, Fang a, b, ⁎
Publikováno v:
In International Immunopharmacology September 2023 122
Akademický článek
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