Towards Fast Computation of Certified Robustness for ReLU Networks

25 April 2018

Inderjit S. Dhillon

Papers citing "Towards Fast Computation of Certified Robustness for ReLU Networks"

50 / 157 papers shown

Title
A Domain-Theoretic Framework for Robustness Analysis of Neural Networks Can Zhou R. A. Shaikh Yiran Li Amin Farjudian OOD 41 4 0 01 Mar 2022
Adversarial robustness of sparse local Lipschitz predictors Ramchandran Muthukumar Jeremias Sulam AAML 34 13 0 26 Feb 2022
Holistic Adversarial Robustness of Deep Learning Models Pin-Yu Chen Sijia Liu AAML 51 16 0 15 Feb 2022
An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks Matan Ostrovsky Clark W. Barrett Guy Katz 40 26 0 06 Jan 2022
On the Minimal Adversarial Perturbation for Deep Neural Networks with Provable Estimation Error Fabio Brau Giulio Rossolini Alessandro Biondi Giorgio Buttazzo AAML 33 7 0 04 Jan 2022
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training Chen Liu Zhichao Huang Mathieu Salzmann Tong Zhang Sabine Süsstrunk AAML 23 13 0 14 Dec 2021
On the Existence of the Adversarial Bayes Classifier (Extended Version) Pranjal Awasthi Natalie Frank M. Mohri 31 24 0 03 Dec 2021
On the Equivalence between Neural Network and Support Vector Machine Yilan Chen Wei Huang Lam M. Nguyen Tsui-Wei Weng AAML 25 18 0 11 Nov 2021
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds Yujia Huang Huan Zhang Yuanyuan Shi J Zico Kolter Anima Anandkumar 41 76 0 02 Nov 2021
ε-weakened Robustness of Deep Neural Networks Pei Huang Yuting Yang Minghao Liu Fuqi Jia Feifei Ma Jian Zhang AAML 27 18 0 29 Oct 2021
Robustness of Graph Neural Networks at Scale Simon Geisler Tobias Schmidt Hakan cSirin Daniel Zügner Aleksandar Bojchevski Stephan Günnemann AAML 30 125 0 26 Oct 2021
AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang Chaowei Xiao Jean Kossaifi Zhiding Yu Anima Anandkumar Zhangyang Wang 27 107 0 26 Oct 2021
Improving Adversarial Robustness for Free with Snapshot Ensemble Yihao Wang AAML UQCV 17 1 0 07 Oct 2021
Neural Network Verification in Control M. Everett AAML 34 16 0 30 Sep 2021
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation Chuangchuang Sun Dong-Ki Kim Jonathan P. How AAML 33 19 0 14 Sep 2021
Shared Certificates for Neural Network Verification Marc Fischer C. Sprecher Dimitar I. Dimitrov Gagandeep Singh Martin Vechev AAML 25 12 0 01 Sep 2021
Reachability Analysis of Neural Feedback Loops M. Everett Golnaz Habibi Chuangchuang Sun Jonathan P. How 19 53 0 09 Aug 2021
Neural Network Branch-and-Bound for Neural Network Verification Florian Jaeckle Jingyue Lu M. P. Kumar 18 8 0 27 Jul 2021
On the Certified Robustness for Ensemble Models and Beyond Zhuolin Yang Linyi Li Xiaojun Xu B. Kailkhura Tao Xie Bo-wen Li AAML 29 48 0 22 Jul 2021
Provable Lipschitz Certification for Generative Models Matt Jordan A. Dimakis 22 14 0 06 Jul 2021
Scalable Certified Segmentation via Randomized Smoothing Marc Fischer Maximilian Baader Martin Vechev 18 38 0 01 Jul 2021
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems Chao Huang Jiameng Fan Zhilu Wang Yixuan Wang Weichao Zhou Jiajun Li Xin Chen Wenchao Li Qi Zhu 40 48 0 25 Jun 2021
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting Shaoru Chen Eric Wong Zico Kolter Mahyar Fazlyab 47 15 0 16 Jun 2021
Pruning and Slicing Neural Networks using Formal Verification O. Lahav Guy Katz 26 20 0 28 May 2021
DNNV: A Framework for Deep Neural Network Verification David Shriver Sebastian G. Elbaum Matthew B. Dwyer 21 31 0 26 May 2021
Analytical bounds on the local Lipschitz constants of ReLU networks Trevor Avant K. Morgansen FAtt 24 12 0 29 Apr 2021
A Review of Formal Methods applied to Machine Learning Caterina Urban Antoine Miné 39 55 0 06 Apr 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Adversarial Training is Not Ready for Robot Learning Mathias Lechner Ramin Hasani Radu Grosu Daniela Rus T. Henzinger AAML 38 34 0 15 Mar 2021
PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations Mark Niklas Muller Gleb Makarchuk Gagandeep Singh Markus Püschel Martin Vechev 41 90 0 05 Mar 2021
Globally-Robust Neural Networks Klas Leino Zifan Wang Matt Fredrikson AAML OOD 80 126 0 16 Feb 2021
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Felix O. Olowononi D. Rawat Chunmei Liu 38 133 0 14 Feb 2021
On the Paradox of Certified Training Nikola Jovanović Mislav Balunović Maximilian Baader Martin Vechev OOD 28 13 0 12 Feb 2021
Fast Training of Provably Robust Neural Networks by SingleProp Akhilan Boopathy Tsui-Wei Weng Sijia Liu Pin-Yu Chen Gaoyuan Zhang Luca Daniel AAML 11 7 0 01 Feb 2021
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification Brendon G. Anderson Ziye Ma Jingqi Li Somayeh Sojoudi 58 1 0 22 Jan 2021
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning Xiaoyang Sean Wang Bo-wen Li Yibo Jacky Zhang B. Kailkhura K. Nahrstedt 18 3 0 15 Jan 2021
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training Theodoros Tsiligkaridis Jay Roberts AAML 22 11 0 22 Dec 2020
DSRNA: Differentiable Search of Robust Neural Architectures Ramtin Hosseini Xingyi Yang P. Xie OOD AAML 29 50 0 11 Dec 2020
Data-Dependent Randomized Smoothing Motasem Alfarra Adel Bibi Philip Torr Guohao Li UQCV 28 34 0 08 Dec 2020
Certified Monotonic Neural Networks Xingchao Liu Xing Han Na Zhang Qiang Liu 24 79 0 20 Nov 2020
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations Jinyuan Jia Binghui Wang Xiaoyu Cao Hongbin Liu Neil Zhenqiang Gong 21 24 0 15 Nov 2020
An SMT-Based Approach for Verifying Binarized Neural Networks Guy Amir Haoze Wu Clark W. Barrett Guy Katz 21 58 0 05 Nov 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 234 680 0 19 Oct 2020
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification Davide Corsi Enrico Marchesini Alessandro Farinelli OffRL 14 2 0 19 Oct 2020
Global Optimization of Objective Functions Represented by ReLU Networks Christopher A. Strong Haoze Wu Aleksandar Zeljić Kyle D. Julian Guy Katz Clark W. Barrett Mykel J. Kochenderfer AAML 17 33 0 07 Oct 2020
Adversarial Boot Camp: label free certified robustness in one epoch Ryan Campbell Chris Finlay Adam M. Oberman AAML 28 0 0 05 Oct 2020
Data-Driven Certification of Neural Networks with Random Input Noise Brendon G. Anderson Somayeh Sojoudi AAML 17 11 0 02 Oct 2020
Deep Learning & Software Engineering: State of Research and Future Directions P. Devanbu Matthew B. Dwyer Sebastian G. Elbaum M. Lowry Kevin Moran Denys Poshyvanyk Baishakhi Ray Rishabh Singh Xiangyu Zhang 11 22 0 17 Sep 2020
SoK: Certified Robustness for Deep Neural Networks Linyi Li Tao Xie Bo-wen Li AAML 33 128 0 09 Sep 2020
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation Sicheng Zhao Xiangyu Yue Shanghang Zhang Bo-wen Li Han Zhao ... Ravi Krishna Joseph E. Gonzalez Alberto L. Sangiovanni-Vincentelli S. Seshia Kurt Keutzer 39 264 0 01 Sep 2020