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Guy Katz
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Reluplex: An efficient SMT solver for verifying deep neural networks
G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer
International conference on computer aided verification, 97-117, 2017
14052017
The marabou framework for verification and analysis of deep neural networks
G Katz, DA Huang, D Ibeling, K Julian, C Lazarus, R Lim, P Shah, ...
International Conference on Computer Aided Verification, 443-452, 2019
2872019
Deepsafe: A data-driven approach for assessing robustness of neural networks
D Gopinath, G Katz, CS Păsăreanu, C Barrett
International symposium on automated technology for verification and …, 2018
1462018
Provably minimally-distorted adversarial examples
N Carlini, G Katz, C Barrett, DL Dill
arXiv preprint arXiv:1709.10207, 2017
1392017
Towards proving the adversarial robustness of deep neural networks
G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer
arXiv preprint arXiv:1709.02802, 2017
1132017
SMTCoq: A plug-in for integrating SMT solvers into Coq
B Ekici, A Mebsout, C Tinelli, C Keller, G Katz, A Reynolds, C Barrett
International Conference on Computer Aided Verification, 126-133, 2017
732017
An abstraction-based framework for neural network verification
YY Elboher, J Gottschlich, G Katz
International Conference on Computer Aided Verification, 43-65, 2020
632020
Verifying deep-RL-driven systems
Y Kazak, C Barrett, G Katz, M Schapira
Proceedings of the 2019 workshop on network meets AI & ML, 83-89, 2019
512019
ScenarioTools–A tool suite for the scenario-based modeling and analysis of reactive systems
J Greenyer, D Gritzner, T Gutjahr, F König, N Glade, A Marron, G Katz
Science of Computer Programming 149, 15-27, 2017
362017
Toward scalable verification for safety-critical deep networks
L Kuper, G Katz, J Gottschlich, K Julian, C Barrett, M Kochenderfer
arXiv preprint arXiv:1801.05950, 2018
352018
On composing and proving the correctness of reactive behavior
D Harel, A Kantor, G Katz, A Marron, L Mizrahi, G Weiss
2013 Proceedings of the International Conference on Embedded Software …, 2013
352013
Minimal Modifications of Deep Neural Networks using Verification.
B Goldberger, G Katz, Y Adi, J Keshet
LPAR 2020, 23rd, 2020
322020
Parallelization techniques for verifying neural networks
H Wu, A Ozdemir, A Zeljic, K Julian, A Irfan, D Gopinath, S Fouladi, G Katz, ...
# PLACEHOLDER_PARENT_METADATA_VALUE# 1, 128-137, 2020
322020
An SMT-based approach for verifying binarized neural networks
G Amir, H Wu, C Barrett, G Katz
International Conference on Tools and Algorithms for the Construction and …, 2021
312021
Non-intrusive repair of reactive programs
D Harel, G Katz, A Marron, G Weiss
2012 IEEE 17th International Conference on Engineering of Complex Computer …, 2012
312012
Scenario-Based Modeling and Synthesis for Reactive Systems with Dynamic System Structure in ScenarioTools.
J Greenyer, D Gritzner, G Katz, A Marron
D&P@ MoDELS, 16-23, 2016
292016
Verifying recurrent neural networks using invariant inference
Y Jacoby, C Barrett, G Katz
International Symposium on Automated Technology for Verification and …, 2020
272020
Lazy proofs for DPLL (T)-based SMT solvers
G Katz, C Barrett, C Tinelli, A Reynolds, L Hadarean
2016 Formal Methods in Computer-Aided Design (FMCAD), 93-100, 2016
272016
Simplifying neural networks using formal verification
S Gokulanathan, A Feldsher, A Malca, C Barrett, G Katz
NASA Formal Methods Symposium, 85-93, 2020
26*2020
Ground-Truth Adversarial Examples
N Carlini, G Katz, C Barrett, DL Dill
arXiv preprint arXiv:1709.10207v1, 2017
242017
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