7 citations · 14 across the 5 of their papers we have counts for
5 papers
Repairing Adversarial Texts through Perturbation
Guoliang Dong, Jingyi Wang, Jun Sun +5
It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predicti…
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Peixin Zhang, Jingyi Wang, Jun Sun +5
Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning appl…
Probabilistic Verification of Neural Networks Against Group Fairness
Bing Sun, Jun Sun, Ting Dai +1
Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of neu…
Rethinking Natural Adversarial Examples for Classification Models
Xiao Li, Jianmin Li, Ting Dai +3
Recently, it was found that many real-world examples without intentional modifications can fool machine learning models, and such examples are called "natural adversarial examples"…
Towards Repairing Neural Networks Correctly
Guoliang Dong, Jun Sun, Jingyi Wang +2
Neural networks are increasingly applied to support decision making in safety-critical applications (like autonomous cars, unmanned aerial vehicles and face recognition based authe…