117 citations · 230 across the 16 of their papers we have counts for
4 papers · 2 filters
A Practical Layer-Parallel Training Algorithm for Residual Networks
Qi Sun, Hexin Dong, Zewei Chen +5
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which a…
Transferred Discrepancy: Quantifying the Difference Between Representations
Yunzhen Feng, Runtian Zhai, Di He +2
Understanding what information neural networks capture is an essential problem in deep learning, and studying whether different models capture similar features is an initial step t…
Enhancing Certified Robustness via Smoothed Weighted Ensembling
Chizhou Liu, Yunzhen Feng, Ranran Wang +1
Randomized smoothing has achieved state-of-the-art certified robustness against -norm adversarial attacks. However, it is not wholly resolved on how to find the optimal base c…
Blind Adversarial Training: Balance Accuracy and Robustness
Haidong Xie, Xueshuang Xiang, Naijin Liu +1
Adversarial training (AT) aims to improve the robustness of deep learning models by mixing clean data and adversarial examples (AEs). Most existing AT approaches can be grouped int…