28 citations · 97 across the 33 of their papers we have counts for
6 papers · 1 filter
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Kaixiong Zhou, Zhenyu Zhang, Shengyuan Chen +4
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advan…
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Tianlong Chen, Huan Zhang, Zhenyu Zhang +4
Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish. Th…
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Tianlong Chen, Zhenyu Zhang, Sijia Liu +3
Pre-training serves as a broadly adopted starting point for transfer learning on various downstream tasks. Recent investigations of lottery tickets hypothesis (LTH) demonstrate suc…
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free
Tianlong Chen, Zhenyu Zhang, Yihua Zhang +3
Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a particular t…
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy
Tianlong Chen, Zhenyu Zhang, Yu Cheng +2
Vision transformers (ViTs) have gained increasing popularity as they are commonly believed to own higher modeling capacity and representation flexibility, than traditional convolut…
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training
Tianlong Chen, Zhenyu Zhang, Pengjun Wang +4
Recent studies demonstrate that deep networks, even robustified by the state-of-the-art adversarial training (AT), still suffer from large robust generalization gaps, in addition t…