16 citations · 18 across the 11 of their papers we have counts for
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cs.LG2023★ 1 cited
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
Qi Yan, Zhengyang Liang, Yang Song +2
Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts,…
cs.LG2023★ 16 cited
Specformer: Spectral Graph Neural Networks Meet Transformers
Deyu Bo, Chuan Shi, Lele Wang +1
Spectral graph neural networks (GNNs) learn graph representations via spectral-domain graph convolutions. However, most existing spectral graph filters are scalar-to-scalar functio…
cs.LG2022
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models
Lele Wang, Bin Liu
Training an ensemble of diverse sub-models has been empirically demonstrated as an effective strategy for improving the adversarial robustness of deep neural networks. However, cur…