Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Mu, Cong"'
Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains, a task complicated by variations in presentation and cognitive nuances. In this paper, we introduce AD-Aligning, a novel approach that combines adver
Externí odkaz:
http://arxiv.org/abs/2405.09582
Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. Therefore, many recent works propose mitigation strategies to avoid noisy
Externí odkaz:
http://arxiv.org/abs/2210.15083
We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel (SBM) in the case where it is prohibitively expensive to observe the entire graph. Theoretically, we provide justification of our proposed Chernoff-opti
Externí odkaz:
http://arxiv.org/abs/2208.13921
Autor:
Chen, Li, Huang, Ningyuan, Mu, Cong, Helm, Hayden S., Lytvynets, Kate, Yang, Weiwei, Priebe, Carey E.
Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the network architecture or careful tuning of the optimization procedure. In
Externí odkaz:
http://arxiv.org/abs/2205.14299
Autor:
Duan, Yinghui, Qu, Wenwen, Chang, Shuxian, Ju, Ming, Wang, Cuiying, Mu, Cong, Cao, Hengchun, Li, Guiting, Tian, Qiuzhen, Ma, Qin, Zhang, Zhanyou, Zhang, Haiyang, Miao, Hongmei
Publikováno v:
In The Crop Journal February 2024 12(1):252-261
Autor:
Miao, Hongmei, Wang, Lei, Qu, Lingbo, Liu, Hongyan, Sun, Yamin, Le, Meiwang, Wang, Qiang, Wei, Shuangling, Zheng, Yongzhan, Lin, Wenchao, Duan, Yinghui, Cao, Hengchun, Xiong, Songjin, Wang, Xuede, Wei, Libin, Li, Chun, Ma, Qin, Ju, Ming, Zhao, Ruihong, Li, Guiting, Mu, Cong, Tian, Qiuzhen, Mei, Hongxian, Zhang, Tide, Gao, Tongmei, Zhang, Haiyang
Publikováno v:
In Plant Communications 8 January 2024 5(1)
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity. Beyond mere adjacency matrices, many real networks also involve vertex
Externí odkaz:
http://arxiv.org/abs/2007.02156
Akademický článek
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Akademický článek
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Publikováno v:
Jisuanji kexue, Vol 48, Iss 10, Pp 152-159 (2021)
The dense subgraph search problem is one of the most important graph analysis problems.It is widely used in many fields,such as the social user correlation analysis in social networks,the community discovery in the Web,etc.However,the current researc
Externí odkaz:
https://doaj.org/article/d68caeef15ef416ab4a226d372c0ee09