20 citations · 77 across the 19 of their papers we have counts for
4 papers · 1 filter
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Wei Huang, Xingyu Zhao, Gaojie Jin +1
Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistingui…
Enhancing Adversarial Training with Second-Order Statistics of Weights
Gaojie Jin, Xinping Yi, Wei Huang +2
Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over mo…
Neuronal Correlation: a Central Concept in Neural Network
Gaojie Jin, Xinping Yi, Xiaowei Huang
This paper proposes to study neural networks through neuronal correlation, a statistical measure of correlated neuronal activity on the penultimate layer. We show that neuronal cor…
Weight Expansion: A New Perspective on Dropout and Generalization
Gaojie Jin, Xinping Yi, Pengfei Yang +3
While dropout is known to be a successful regularization technique, insights into the mechanisms that lead to this success are still lacking. We introduce the concept of \emph{weig…