Towards Evaluating and Training Verifiably Robust Neural Networks

Towards Evaluating and Training Verifiably Robust Neural Networks

1 April 2021

Kehuan Zhang

Papers citing "Towards Evaluating and Training Verifiably Robust Neural Networks"

11 / 11 papers shown

Title
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation Haitham Khedr Yasser Shoukry 52 5 0 22 May 2023
Boosting Verified Training for Robust Image Classifications via Abstraction Zhaodi Zhang Zhiyi Xue Yang Chen Si Liu Yueling Zhang Jiaheng Liu Min Zhang 56 4 0 21 Mar 2023
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks Xingwu Guo Ziwei Zhou Yueling Zhang Guy Katz Hao Fei AAML 37 5 0 27 Jan 2023
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound Alessandro De Palma Rudy Bunel Krishnamurthy Dvijotham M. P. Kumar Robert Stanforth AAML 50 17 0 29 Jun 2022
Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions Marwan Omar Soohyeon Choi Daehun Nyang David A. Mohaisen 32 57 0 03 Jan 2022
On the Paradox of Certified Training Nikola Jovanović Mislav Balunović Maximilian Baader Martin Vechev OOD 28 13 0 12 Feb 2021
SoK: Certified Robustness for Deep Neural Networks Linyi Li Tao Xie Bo Li AAML 38 128 0 09 Sep 2020
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks Akhilan Boopathy Tsui-Wei Weng Pin-Yu Chen Sijia Liu Luca Daniel AAML 110 138 0 29 Nov 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 251 1,842 0 03 Feb 2017
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Zhuowen Tu Kaiming He 348 10,237 0 16 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 359 5,849 0 08 Jul 2016