158 citations · 212 across the 8 of their papers we have counts for
12 papers · 1 filter
MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks
Xinling Yu, José E. C. Serrallés, Ilias I. Giannakopoulos +4
Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP can be potential biomark…
Fast Training of Provably Robust Neural Networks by SingleProp
Akhilan Boopathy, Tsui-Wei Weng, Sijia Liu +3
Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees. However, these techniques can be computationally cos…
Higher-Order Certification for Randomized Smoothing
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng +3
Randomized smoothing is a recently proposed defense against adversarial attacks that has achieved SOTA provable robustness against perturbations. A number of publications…
Proper Network Interpretability Helps Adversarial Robustness in Classification
Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang +4
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visual…
Hidden Cost of Randomized Smoothing
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei +4
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public. While immense interests were in either crafting adve…
Fastened CROWN: Tightened Neural Network Robustness Certificates
Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong +3
The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reli…