6 citations · 6 across the 4 of their papers we have counts for
4 papers
Shapley-Guided Neural Repair Approach via Derivative-Free Optimization
Xinyu Sun, Wanwei Liu, Haoang Chi +7
DNNs are susceptible to defects like backdoors, adversarial attacks, and unfairness, undermining their reliability. Existing approaches mainly involve retraining, optimization, con…
FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection
Jialuo Chen, Jingyi Wang, Xiyue Zhang +4
Due to the vast testing space, the increasing demand for effective and efficient testing of deep neural networks (DNNs) has led to the development of various DNN test case prioriti…
Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
Xiaokun Luan, Xiyue Zhang, Jingyi Wang +1
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and…
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Haibin Zheng, Zhiqing Chen, Tianyu Du +6
Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions espe…