73 citations · 142 across the 31 of their papers we have counts for
4 papers · 1 filter
Robustness, Privacy, and Generalization of Adversarial Training
Fengxiang He, Shaopeng Fu, Bohan Wang +1
Adversarial training can considerably robustify deep neural networks to resist adversarial attacks. However, some works suggested that adversarial training might comprise the priva…
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks
Bohan Wang, Qi Meng, Wei Chen +1
Despite their overwhelming capacity to overfit, deep neural networks trained by specific optimization algorithms tend to generalize well to unseen data. Recently, researchers expla…
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms
Fengxiang He, Bohan Wang, Dacheng Tao
This paper studies the relationship between generalization and privacy preservation in iterative learning algorithms by two sequential steps. We first establish an alignment betwee…
Piecewise linear activations substantially shape the loss surfaces of neural networks
Fengxiang He, Bohan Wang, Dacheng Tao
Understanding the loss surface of a neural network is fundamentally important to the understanding of deep learning. This paper presents how piecewise linear activation functions s…