7 citations · 25 across the 5 of their papers we have counts for
5 papers
Robust Unlearnable Examples: Protecting Data Against Adversarial Learning
Shaopeng Fu, Fengxiang He, Yang Liu +2
The tremendous amount of accessible data in cyberspace face the risk of being unauthorized used for training deep learning models. To address this concern, methods are proposed to…
Knowledge Removal in Sampling-based Bayesian Inference
Shaopeng Fu, Fengxiang He, Dacheng Tao
The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, compa…
Bayesian Inference Forgetting
Shaopeng Fu, Fengxiang He, Yue Xu +1
The right to be forgotten has been legislated in many countries but the enforcement in machine learning would cause unbearable costs: companies may need to delete whole models lear…
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…
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…