4 citations · 14 across the 28 of their papers we have counts for
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cs.LG2026
Neural Approximation and Its Applications
Wei-Hao Wu, Ting-Zhu Huang, Xi-Le Zhao +2
Multivariate function approximation is a fundamental problem in machine learning. Classic multivariate function approximations rely on hand-crafted basis functions (e.g., polynomia…
cs.LG2024
SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples
Yuntao Shou, Xiangyong Cao, Deyu Meng
Graph Contrastive Learning (GCL) excels at managing noise and fluctuations in input data, making it popular in various fields (e.g., social networks, and knowledge graphs). Our stu…
cs.LG2024
Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis
Haodong Wen, Bodong Du, Ruixun Liu +2
Recently, the optimization of polynomial filters within Spectral Graph Neural Networks (GNNs) has emerged as a prominent research focus. Existing spectral GNNs mainly emphasize pol…