Towards Certifying L-infinity Robustness using Neural Networks with
L-inf-dist Neurons

Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons

10 February 2021

Tianle Cai

Papers citing "Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons"

10 / 10 papers shown

Title
Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks Ruigang Wang Krishnamurthy Dvijotham I. Manchester 19 5 0 02 Feb 2024
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization Mahyar Fazlyab Taha Entesari Aniket Roy Ramalingam Chellappa AAML 11 11 0 29 Sep 2023
Towards Robust Dataset Learning Yihan Wu Xinda Li Florian Kerschbaum Heng Huang Hongyang R. Zhang DD OOD 28 10 0 19 Nov 2022
Certified Training: Small Boxes are All You Need Mark Niklas Muller Franziska Eckert Marc Fischer Martin Vechev AAML 20 45 0 10 Oct 2022
A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables Adrien Benamira Tristan Guérand Thomas Peyrin Trevor Yap Bryan Hooi 27 1 0 18 Aug 2022
Provably Adversarially Robust Nearest Prototype Classifiers Václav Voráček Matthias Hein AAML 15 11 0 14 Jul 2022
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound Alessandro De Palma Rudy Bunel Krishnamurthy Dvijotham M. P. Kumar Robert Stanforth AAML 32 17 0 29 Jun 2022
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective Mark Huasong Meng Guangdong Bai Sin Gee Teo Zhe Hou Yan Xiao Yun Lin J. Dong AAML 10 43 0 24 Jun 2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power Binghui Li Jikai Jin Han Zhong J. Hopcroft Liwei Wang OOD 53 27 0 27 May 2022
On the Paradox of Certified Training Nikola Jovanović Mislav Balunović Maximilian Baader Martin Vechev OOD 15 13 0 12 Feb 2021