55 citations · 84 across the 8 of their papers we have counts for
8 papers
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…
Learning Task-Specific Strategies for Accelerated MRI
Zihui Wu, Tianwei Yin, Yu Sun +4
Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separ…
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…
End-to-End Sequential Sampling and Reconstruction for MRI
Tianwei Yin, Zihui Wu, He Sun +3
Accelerated MRI shortens acquisition time by subsampling in the measurement -space. Recovering a high-fidelity anatomical image from subsampled measurements requires close coope…
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…
SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors
Zihui Wu, Yu Sun, Alex Matlock +3
Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large d…
Online Regularization by Denoising with Applications to Phase Retrieval
Zihui Wu, Yu Sun, Jiaming Liu +1
Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems. Most RED algorithms are iterative batch procedures, which limits their applicability…