Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

3 February 2017

Mykel Kochenderfer

Papers citing "Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks"

50 / 346 papers shown

Title
Probabilistic Guarantees for Safe Deep Reinforcement Learning E. Bacci David Parker 16 27 0 14 May 2020
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data Lu Wang Huan Zhang Jinfeng Yi Cho-Jui Hsieh Yuan Jiang AAML 35 12 0 11 May 2020
A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems Anthony Corso Robert J. Moss Mark Koren Ritchie Lee Mykel J. Kochenderfer 19 169 0 06 May 2020
Robustness Certification of Generative Models M. Mirman Timon Gehr Martin Vechev AAML 43 22 0 30 Apr 2020
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty Xiyue Zhang Xiaofei Xie Lei Ma Xiaoning Du Q. Hu Yang Liu Jianjun Zhao Meng Sun AAML 10 76 0 24 Apr 2020
Probabilistic Safety for Bayesian Neural Networks Matthew Wicker Luca Laurenti A. Patané Marta Z. Kwiatkowska AAML 14 52 0 21 Apr 2020
Parallelization Techniques for Verifying Neural Networks Haoze Wu Alex Ozdemir Aleksandar Zeljić A. Irfan Kyle D. Julian D. Gopinath Sadjad Fouladi Guy Katz C. Păsăreanu Clark W. Barrett 24 59 0 17 Apr 2020
NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems Hoang-Dung Tran Xiaodong Yang Diego Manzanas Lopez Patrick Musau L. V. Nguyen Weiming Xiang Stanley Bak Taylor T. Johnson 29 237 0 12 Apr 2020
Verification of Deep Convolutional Neural Networks Using ImageStars Hoang-Dung Tran Stanley Bak Weiming Xiang Taylor T. Johnson AAML 20 127 0 12 Apr 2020
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning Michael Everett Bjorn Lutjens Jonathan P. How AAML 13 41 0 11 Apr 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 75 98 0 20 Mar 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 44 82 0 17 Mar 2020
A Safety Framework for Critical Systems Utilising Deep Neural Networks Xingyu Zhao Alec Banks James Sharp Valentin Robu David Flynn Michael Fisher Xiaowei Huang AAML 50 48 0 07 Mar 2020
Exploiting Verified Neural Networks via Floating Point Numerical Error Kai Jia Martin Rinard AAML 37 34 0 06 Mar 2020
A Robust Speaker Clustering Method Based on Discrete Tied Variational Autoencoder Chen Feng Jianzong Wang Tongxu Li Junqing Peng Jing Xiao DRL 11 0 0 04 Mar 2020
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1 Robin Bloomfield Gareth Fletcher Heidy Khlaaf Philippa Ryan Shuji Kinoshita ... M. Takeyama Yamato Matsubara Peter Popov Peter Popov Kazuki Imai Yoshinori Tsutake 31 3 0 28 Feb 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 47 785 0 26 Feb 2020
Adversarial Ranking Attack and Defense Mo Zhou Zhenxing Niu Le Wang Qilin Zhang G. Hua 36 38 0 26 Feb 2020
Gödel's Sentence Is An Adversarial Example But Unsolvable Xiaodong Qi Lansheng Han AAML 20 0 0 25 Feb 2020
Lagrangian Decomposition for Neural Network Verification Rudy Bunel Alessandro De Palma Alban Desmaison Krishnamurthy Dvijotham Pushmeet Kohli Philip Torr M. P. Kumar 19 50 0 24 Feb 2020
Learning Certified Individually Fair Representations Anian Ruoss Mislav Balunović Marc Fischer Martin Vechev FaML 15 92 0 24 Feb 2020
Robustness Verification for Transformers Zhouxing Shi Huan Zhang Kai-Wei Chang Minlie Huang Cho-Jui Hsieh AAML 19 104 0 16 Feb 2020
Importance-Driven Deep Learning System Testing Simos Gerasimou Hasan Ferit Eniser A. Sen Alper Çakan AAML VLM 30 98 0 09 Feb 2020
Semantic Robustness of Models of Source Code Goutham Ramakrishnan Jordan Henkel Zi Wang Aws Albarghouthi S. Jha Thomas W. Reps SILM AAML 37 97 0 07 Feb 2020
Adversarial Machine Learning -- Industry Perspectives Ramnath Kumar Magnus Nyström J. Lambert Andrew Marshall Mario Goertzel Andi Comissoneru Matt Swann Sharon Xia AAML SILM 29 232 0 04 Feb 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 87 1,158 0 12 Jan 2020
ReluDiff: Differential Verification of Deep Neural Networks Brandon Paulsen Jingbo Wang Chao Wang 22 53 0 10 Jan 2020
Combining Deep Learning and Verification for Precise Object Instance Detection Siddharth Ancha Junyu Nan David Held 23 3 0 27 Dec 2019
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks Yizhen Dong Peixin Zhang Jingyi Wang Shuang Liu Jun Sun Jianye Hao Xinyu Wang Li Wang J. Dong Ting Dai OOD AAML 19 32 0 14 Nov 2019
Enhancing Certifiable Robustness via a Deep Model Ensemble Huan Zhang Minhao Cheng Cho-Jui Hsieh 33 9 0 31 Oct 2019
An Abstraction-Based Framework for Neural Network Verification Y. Elboher Justin Emile Gottschlich Guy Katz 27 122 0 31 Oct 2019
Simplifying Neural Networks using Formal Verification S. Gokulanathan Alexander Feldsher Adi Malca Clark W. Barrett Guy Katz 30 4 0 25 Oct 2019
Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller Radoslav Ivanov Taylor J. Carpenter James Weimer Rajeev Alur George J. Pappas Insup Lee 15 78 0 24 Oct 2019
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms Qianyu Guo Sen Chen Xiaofei Xie Lei Ma Q. Hu Hongtao Liu Yang Liu Jianjun Zhao Xiaohong Li 33 122 0 15 Sep 2019
Defending Against Adversarial Iris Examples Using Wavelet Decomposition Sobhan Soleymani Ali Dabouei J. Dawson Nasser M. Nasrabadi AAML 27 9 0 08 Aug 2019
ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks Xuankang Lin He Zhu R. Samanta Suresh Jagannathan AAML 27 28 0 17 Jul 2019
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack Francesco Croce Matthias Hein AAML 43 474 0 03 Jul 2019
Robustness Guarantees for Deep Neural Networks on Videos Min Wu Marta Z. Kwiatkowska AAML 17 22 0 28 Jun 2019
Verifying Robustness of Gradient Boosted Models Gil Einziger M. Goldstein Yaniv Saár Itai Segall 23 41 0 26 Jun 2019
ReachNN: Reachability Analysis of Neural-Network Controlled Systems Chao Huang Jiameng Fan Wenchao Li Xin Chen Qi Zhu 23 78 0 25 Jun 2019
Quantitative Verification of Neural Networks And its Security Applications Teodora Baluta Shiqi Shen Shweta Shinde Kuldeep S. Meel P. Saxena AAML 16 104 0 25 Jun 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 24 739 0 19 Jun 2019
Robustness Verification of Tree-based Models Hongge Chen Huan Zhang Si Si Yang Li Duane S. Boning Cho-Jui Hsieh AAML 17 76 0 10 Jun 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Hadi Salman Greg Yang Jungshian Li Pengchuan Zhang Huan Zhang Ilya P. Razenshteyn Sébastien Bubeck AAML 31 535 0 09 Jun 2019
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks Maksym Andriushchenko Matthias Hein 20 61 0 08 Jun 2019
Risks from Learned Optimization in Advanced Machine Learning Systems Evan Hubinger Chris van Merwijk Vladimir Mikulik Joar Skalse Scott Garrabrant 42 146 0 05 Jun 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 44 317 0 27 May 2019
Testing DNN Image Classifiers for Confusion & Bias Errors Yuchi Tian Ziyuan Zhong Vicente Ordonez Gail E. Kaiser Baishakhi Ray 24 52 0 20 May 2019
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain Lei Bu Yuchao Duan Fu Song Zhe Zhao AAML 32 18 0 19 May 2019
POPQORN: Quantifying Robustness of Recurrent Neural Networks Ching-Yun Ko Zhaoyang Lyu Tsui-Wei Weng Luca Daniel Ngai Wong Dahua Lin AAML 17 75 0 17 May 2019