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The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models.
Software Engineering Techniques: Report on a Conference Sponsored by the NATO Science Committee
Buxton, John N and Randell, Brian · 1970
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A hierarchy of relaxations and convex hull characterizations for mixed-integer zero—one programming problems
Sherali, Hanif D and Adams, Warren P · 1994
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Grasp: A search algorithm for propositional satisfiability
Marques-Silva, João P and Sakallah, Karem A · 1999
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Interval arithmetic: From principles to implementation
Hickey, Timothy, Ju, Qun, and Van Emden, Maarten H · 2001
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Verification of a trained neural network accuracy
Zakrzewski, Radosiaw R · 2001
Earlier work this paper cites.
Splitting on demand in sat modulo theories
Barrett, Clark, Nieuwenhuis, Robert, Oliveras, Albert, and Tinelli, Cesare · 2006
Cited alongside, same era.
An abstraction-refinement approach to verification of artificial neural networks
Pulina, Luca and Tacchella, Armando · 2010
Cited alongside, same era.
Measuring neural net robustness with constraints
Bastani, Osbert, Ioannou, Yani, Lampropoulos, Leonidas, Vytiniotis, Dimitrios, Nori, Aditya, and Criminisi, Antonio · 2016
Cited alongside, same era.
Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, Matthias and Andriushchenko, Maksym · 2017
Cited alongside, same era.
Safety verification of deep neural networks
Huang, Xiaowei, Kwiatkowska, Marta, Wang, Sen, and Wu, Min · 2017
Cited alongside, same era.
Maximum resilience of artificial neural networks
Cheng, Chih-Hong, Nührenberg, Georg, and Ruess, Harald
Cited in the paper.
Verification of binarized neural networks
Cheng, Chih-Hong, Nührenberg, Georg, and Ruess, Harald
Cited in the paper.
Formal verification of piece-wise linear feed-forward neural networks
Ehlers, Ruediger
Cited in the paper.
Reluplex: An efficient smt solver for verifying deep neural networks
Katz, Guy, Barrett, Clark, Dill, David, Julian, Kyle, and Kochenderfer, Mykel
Cited in the paper.
Reluplex
Katz, Guy, Barrett, Clark, Dill, David, Julian, Kyle, and Kochenderfer, Mykel
Cited in the paper.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Kolter, Zico and Wong, Eric · 2017
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An approach to reachability analysis for feed-forward relu neural networks
Lomuscio, Alessio and Maganti, Lalit · 2017
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Verifying properties of binarized deep neural networks
Narodytska, Nina, Kasiviswanathan, Shiva Prasad, Ryzhyk, Leonid, Sagiv, Mooly, and Walsh, Toby · 2017
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Verifying neural networks with mixed integer programming
Tjeng, Vincent and Tedrake, Russ · 2017
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Output reachable set estimation and verification for multi-layer neural networks
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Xiang, Weiming, Tran, Hoang-Dung, and Johnson, Taylor T · 2017
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