55 citations · 84 across the 8 of their papers we have counts for
4 papers · 1 filter
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
Xinyi Wu, Amir Ajorlou, Zihui Wu +1
Oversmoothing in Graph Neural Networks (GNNs) refers to the phenomenon where increasing network depth leads to homogeneous node representations. While previous work has established…
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training
Zihui Wu, Haichang Gao, Bingqian Zhou +2
In this paper, we investigate on improving the adversarial robustness obtained in adversarial training (AT) via reducing the difficulty of optimization. To better study this proble…
Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization
Shudong Zhang, Haichang Gao, Tianwei Zhang +2
Adversarial training (AT) has proven to be one of the most effective ways to defend Deep Neural Networks (DNNs) against adversarial attacks. However, the phenomenon of robust overf…
Scalable Plug-and-Play ADMM with Convergence Guarantees
Yu Sun, Zihui Wu, Xiaojian Xu +2
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the st…