Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective

4 October 2022

Papers citing "Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective"

8 / 8 papers shown

Title
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification Dongyoon Yang Jihu Lee Yongdai Kim 24 0 0 10 May 2025
On Using Certified Training towards Empirical Robustness Alessandro De Palma Serge Durand Zakaria Chihani François Terrier Caterina Urban OOD AAML 33 1 0 02 Oct 2024
Physics-Driven AI Correction in Laser Absorption Sensing Quantification Ruiyuan Kang P. Liatsis Meixia Geng Qingjie Yang 32 0 0 20 Aug 2024
Raising the Bar for Certified Adversarial Robustness with Diffusion Models Thomas Altstidl David Dobre Björn Eskofier Gauthier Gidel Leo Schwinn DiffM 20 7 0 17 May 2023
Certified Training: Small Boxes are All You Need Mark Niklas Muller Franziska Eckert Marc Fischer Martin Vechev AAML 18 45 0 10 Oct 2022
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100 Sahil Singla Surbhi Singla S. Feizi AAML 30 54 0 05 Aug 2021
Globally-Robust Neural Networks Klas Leino Zifan Wang Matt Fredrikson AAML OOD 80 125 0 16 Feb 2021
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 222 1,832 0 03 Feb 2017