Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Wang, Haohui"'
Long-tailed data distributions are prevalent in a variety of domains, including finance, e-commerce, biomedical science, and cyber security. In such scenarios, the performance of machine learning models is often dominated by the head categories, whil
Externí odkaz:
http://arxiv.org/abs/2307.08235
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect the prediction performance of graph neural networks (GNNs)? In this pa
Externí odkaz:
http://arxiv.org/abs/2306.14340
Autor:
Wang, Haohui, Jing, Baoyu, Ding, Kaize, Zhu, Yada, Cheng, Wei, Zhang, Si, Fan, Yonghui, Zhang, Liqing, Zhou, Dawei
In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitigate class imbalances and enhance the overall performance. Despite the
Externí odkaz:
http://arxiv.org/abs/2305.09938
Autor:
Wang, Haohui, Mao, Yuzhen, Yan, Yujun, Yang, Yaoqing, Sun, Jianhui, Choi, Kevin, Veeramani, Balaji, Hu, Alison, Bowen, Edward, Cody, Tyler, Zhou, Dawei
Non-IID transfer learning on graphs is crucial in many high-stakes domains. The majority of existing works assume stationary distribution for both source and target domains. However, real-world graphs are intrinsically dynamic, presenting challenges
Externí odkaz:
http://arxiv.org/abs/2305.00664
Autor:
Yao, Liuyi, Gao, Dawei, Wang, Zhen, Xie, Yuexiang, Kuang, Weirui, Chen, Daoyuan, Wang, Haohui, Dong, Chenhe, Ding, Bolin, Li, Yaliang
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms
Externí odkaz:
http://arxiv.org/abs/2206.03436
Publikováno v:
In Journal of Cleaner Production 10 September 2024 470
Autor:
Yang, Jie, Sun, Jinsheng, Wang, Ren, Qu, Yuanzhi, Liu, Fan, Feng, Xiaohua, Wang, Haohui, Yang, Zexing, Ren, Han, Liu, Luman
Publikováno v:
In Journal of Molecular Liquids 15 April 2024 400
Akademický článek
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Publikováno v:
In Renewable Energy April 2023 206:1007-1019
Autor:
Wang, Haohui1 (AUTHOR) 1202221411@cug.edu.cn, Wu, Lunwen2 (AUTHOR), Peng, Gang1 (AUTHOR), Du, Hongmei3 (AUTHOR) 1202221411@cug.edu.cn
Publikováno v:
PLoS ONE. 10/26/2023, Vol. 18 Issue 10, p1-27. 27p.