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ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks

17 July 2019

Papers citing "ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks"

37 / 37 papers shown

Title
Provably-Safe Neural Network Training Using Hybrid Zonotope Reachability Analysis Long Kiu Chung Shreyas Kousik 501 0 0 22 Jan 2025
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 526 42,559 0 03 Dec 2019
An Inductive Synthesis Framework for Verifiable Reinforcement Learning He Zhu Zikang Xiong Stephen Magill Suresh Jagannathan 60 97 0 16 Jul 2019
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness Greg Anderson Shankara Pailoor Işıl Dillig Swarat Chaudhuri AAML 78 101 0 22 Apr 2019
Verification of Non-Linear Specifications for Neural Networks Chongli Qin Krishnamurthy Dvijotham Dvijotham Brendan O'Donoghue Rudy Bunel Robert Stanforth Sven Gowal J. Uesato G. Swirszcz Pushmeet Kohli AAML 60 44 0 25 Feb 2019
Semidefinite relaxations for certifying robustness to adversarial examples Aditi Raghunathan Jacob Steinhardt Percy Liang AAML 100 439 0 02 Nov 2018
Efficient Neural Network Robustness Certification with General Activation Functions Huan Zhang Tsui-Wei Weng Pin-Yu Chen Cho-Jui Hsieh Luca Daniel AAML 96 763 0 02 Nov 2018
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models Sven Gowal Krishnamurthy Dvijotham Robert Stanforth Rudy Bunel Chongli Qin J. Uesato Relja Arandjelović Timothy A. Mann Pushmeet Kohli AAML 82 558 0 30 Oct 2018
Efficient Formal Safety Analysis of Neural Networks Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang Suman Jana AAML 70 404 0 19 Sep 2018
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Y. Xiao Vincent Tjeng Nur Muhammad (Mahi) Shafiullah Aleksander Madry AAML OOD 41 201 0 09 Sep 2018
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge Pasquale Minervini Sebastian Riedel AAML NAI GAN 59 119 0 26 Aug 2018
Scaling provable adversarial defenses Eric Wong Frank R. Schmidt J. H. Metzen J. Zico Kolter AAML 78 450 0 31 May 2018
Training verified learners with learned verifiers Krishnamurthy Dvijotham Sven Gowal Robert Stanforth Relja Arandjelović Brendan O'Donoghue J. Uesato Pushmeet Kohli OOD 62 169 0 25 May 2018
Formal Security Analysis of Neural Networks using Symbolic Intervals Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang Suman Jana AAML 84 478 0 28 Apr 2018
Towards Fast Computation of Certified Robustness for ReLU Networks Tsui-Wei Weng Huan Zhang Hongge Chen Zhao Song Cho-Jui Hsieh Duane S. Boning Inderjit S. Dhillon Luca Daniel AAML 108 695 0 25 Apr 2018
A Dual Approach to Scalable Verification of Deep Networks Krishnamurthy Dvijotham Dvijotham Robert Stanforth Sven Gowal Timothy A. Mann Pushmeet Kohli 56 399 0 17 Mar 2018
Certified Defenses against Adversarial Examples Aditi Raghunathan Jacob Steinhardt Percy Liang AAML 113 969 0 29 Jan 2018
Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study M. Fischetti Jason Jo 50 81 0 17 Dec 2017
A Semantic Loss Function for Deep Learning with Symbolic Knowledge Jingyi Xu Zilu Zhang Tal Friedman Yitao Liang Guy Van den Broeck 97 452 0 29 Nov 2017
Learning Explanatory Rules from Noisy Data Richard Evans Edward Grefenstette 122 487 0 13 Nov 2017
Provable defenses against adversarial examples via the convex outer adversarial polytope Eric Wong J. Zico Kolter AAML 128 1,504 0 02 Nov 2017
Provably Minimally-Distorted Adversarial Examples Nicholas Carlini Guy Katz Clark W. Barrett D. Dill AAML 73 89 0 29 Sep 2017
Safe Reinforcement Learning via Shielding Mohammed Alshiekh Roderick Bloem Rüdiger Ehlers Bettina Könighofer S. Niekum Ufuk Topcu 82 690 0 29 Aug 2017
An approach to reachability analysis for feed-forward ReLU neural networks A. Lomuscio Lalit Maganti 65 359 0 22 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 312 12,117 0 19 Jun 2017
Imposing Hard Constraints on Deep Networks: Promises and Limitations Pablo Márquez-Neila Mathieu Salzmann Pascal Fua PINN UQCV 145 140 0 07 Jun 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems Kexin Pei Yinzhi Cao Junfeng Yang Suman Jana AAML 102 1,371 0 18 May 2017
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks Rüdiger Ehlers 104 626 0 03 May 2017
Maximum Resilience of Artificial Neural Networks Chih-Hong Cheng Georg Nührenberg Harald Ruess AAML 115 284 0 28 Apr 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 318 1,874 0 03 Feb 2017
Harnessing Deep Neural Networks with Logic Rules Zhiting Hu Xuezhe Ma Zhengzhong Liu Eduard H. Hovy Eric Xing AI4CE NAI 58 614 0 21 Mar 2016
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 113 3,077 0 14 Nov 2015
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation Deepak Pathak Philipp Krahenbuhl Trevor Darrell SSeg 96 614 0 11 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.0K 150,260 0 22 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 280 19,107 0 20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 171 3,275 0 05 Dec 2014
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning Stéphane Ross Geoffrey J. Gordon J. Andrew Bagnell OffRL 231 3,232 0 02 Nov 2010