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pro vyhledávání: '"Chen, Xuexin"'
Autor:
Chen, Xuexin, Cai, Ruichu, Zheng, Kaitao, Jiang, Zhifan, Huang, Zhengting, Hao, Zhifeng, Li, Zijian
Graph Out-of-Distribution (OOD), requiring that models trained on biased data generalize to the unseen test data, has considerable real-world applications. One of the most mainstream methods is to extract the invariant subgraph by aligning the origin
Externí odkaz:
http://arxiv.org/abs/2407.15273
Autor:
Cai, Ruichu, Jiang, Zhifang, Li, Zijian, Chen, Weilin, Chen, Xuexin, Hao, Zhifeng, Shen, Yifan, Chen, Guangyi, Zhang, Kun
Existing methods for multi-modal time series representation learning aim to disentangle the modality-shared and modality-specific latent variables. Although achieving notable performances on downstream tasks, they usually assume an orthogonal latent
Externí odkaz:
http://arxiv.org/abs/2405.16083
Autor:
Chen, Xuexin, Cai, Ruichu, Zheng, Kaitao, Jiang, Zhifan, Huang, Zhengting, Hao, Zhifeng, Li, Zijian
Graph Out-of-Distribution (OOD), requiring that models trained on biased data generalize to the unseen test data, has a massive of real-world applications. One of the most mainstream methods is to extract the invariant subgraph by aligning the origin
Externí odkaz:
http://arxiv.org/abs/2402.09165
Autor:
Chen, Xuexin, Cai, Ruichu, Huang, Zhengting, Zhu, Yuxuan, Horwood, Julien, Hao, Zhifeng, Li, Zijian, Hernandez-Lobato, Jose Miguel
We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests. Despite their utility, FAMs struggle to distinguish the contribution
Externí odkaz:
http://arxiv.org/abs/2402.08845
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining appr
Externí odkaz:
http://arxiv.org/abs/2212.07056
Autor:
Yu, Qianyi, Yuan, Ruizhong, Zhang, Han, Shu, Xiaohan, Zhu, Jiachen, Liu, Zhengling, Ye, Xiqian, Zhan, Youguo, Tang, Pu, Chen, Xuexin
Publikováno v:
In Journal of Asia-Pacific Entomology September 2024 27(3)
Publikováno v:
In Cytokine August 2024 180
Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph repr
Externí odkaz:
http://arxiv.org/abs/2112.14900
Autor:
Shi, Wenqi, Zhang, Qichao, Sheng, Yifeng, Dong, Zhi, Feng, Ting, Zhang, Junwei, Yu, Longtao, Xu, Zixuan, Pang, Lan, Chen, Jiani, Chen, Xuexin, Huang, Jianhua
Publikováno v:
In Science of the Total Environment 10 January 2024 907
Akademický článek
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