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cs.LG2024
OTAD: An Optimal Transport-Induced Robust Model for Agnostic Adversarial Attack
Kuo Gai, Sicong Wang, Shihua Zhang
Deep neural networks (DNNs) are vulnerable to small adversarial perturbations of the inputs, posing a significant challenge to their reliability and robustness. Empirical methods s…
stat.ML2024
Analytical Solution of a Three-layer Network with a Matrix Exponential Activation Function
Kuo Gai, Shihua Zhang
In practice, deeper networks tend to be more powerful than shallow ones, but this has not been understood theoretically. In this paper, we find the analytical solution of a three-l…
cs.LG2024
Progressive Feedforward Collapse of ResNet Training
Sicong Wang, Kuo Gai, Shihua Zhang
Neural collapse (NC) is a simple and symmetric phenomenon for deep neural networks (DNNs) at the terminal phase of training, where the last-layer features collapse to their class m…