7 citations · 8 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
Parameterizing Activation Functions for Adversarial Robustness
Sihui Dai, Saeed Mahloujifar, Prateek Mittal
Deep neural networks are known to be vulnerable to adversarially perturbed inputs. A commonly used defense is adversarial training, whose performance is influenced by model capacit…
cs.LG2020
Neural Networks with Recurrent Generative Feedback
Yujia Huang, James Gornet, Sihui Dai +4
Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The B…
cs.LG2019★ 7 cited
Out-of-Distribution Detection Using Neural Rendering Generative Models
Yujia Huang, Sihui Dai, Tan Nguyen +2
Out-of-distribution (OoD) detection is a natural downstream task for deep generative models, due to their ability to learn the input probability distribution. There are mainly two…