107 citations · 380 across the 45 of their papers we have counts for
5 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…
Recent advances in deep learning theory
Fengxiang He, Dacheng Tao
Deep learning is usually described as an experiment-driven field under continuous criticizes of lacking theoretical foundations. This problem has been partially fixed by a large vo…
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting
Zeke Xie, Fengxiang He, Shaopeng Fu +3
Deep learning is often criticized by two serious issues which rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labell…
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…