6 citations · 6 across the 5 of their papers we have counts for
5 papers
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
Binghui Li, Yuanzhi Li
Adversarial training is a widely-applied approach to training deep neural networks to be robust against adversarial perturbation. However, although adversarial training has achieve…
On the Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics
Binghui Li, Yuanzhi Li
Similar to surprising performance in the standard deep learning, deep nets trained by adversarial training also generalize well for unseen clean data (natural data). However, despi…
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Binghui Li, Jikai Jin, Han Zhong +2
It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, a…
Reconstruction Task Finds Universal Winning Tickets
Ruichen Li, Binghui Li, Qi Qian +1
Pruning well-trained neural networks is effective to achieve a promising accuracy-efficiency trade-off in computer vision regimes. However, most of existing pruning algorithms only…
Boosting Certified Robustness with EMA Method and Ensemble Model
Binghui Li, Shiji Xin, Qizhe Zhang
The neural network with -Lipschitz property based on -dist neuron has a theoretical guarantee in certified robustness. However, due to the inherent di…