2 citations · 3 across the 3 of their papers we have counts for
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cs.LG2022★ 1 cited
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network
Huan Hua, Jun Yan, Xi Fang +3
The information bottleneck (IB) method is a feasible defense solution against adversarial attacks in deep learning. However, this method suffers from the spurious correlation, whic…
cs.LG2021
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
Wei Zhou, Dong Chen, Jun Yan +3
Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving an…
cs.LG2021★ 2 cited
On Procedural Adversarial Noise Attack And Defense
Jun Yan, Xiaoyang Deng, Huilin Yin +1
Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small perturbations on the input images. Rese…