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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
Computer Aided Verification: 29th International Conference, CAV 2017 …, 2017
23492017
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, ...
Computer Aided Verification: 31st International Conference, CAV 2019, New …, 2019
6632019
An abstraction-based framework for neural network verification
YY Elboher, J Gottschlich, G Katz
Computer Aided Verification: 32nd International Conference, CAV 2020, Los …, 2020
1532020
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
1522017
Deepsafe: A data-driven approach for assessing robustness of neural networks
D Gopinath, G Katz, CS Păsăreanu, C Barrett
Automated Technology for Verification and Analysis: 16th International …, 2018
1322018
Provably minimally-distorted adversarial examples
N Carlini, G Katz, C Barrett, DL Dill
arXiv preprint arXiv:1709.10207, 2017
1202017
SMTCoq: A plug-in for integrating SMT solvers into Coq
B Ekici, A Mebsout, C Tinelli, C Keller, G Katz, A Reynolds, C Barrett
Computer Aided Verification: 29th International Conference, CAV 2017 …, 2017
1202017
Minimal Modifications of Deep Neural Networks using Verification.
B Goldberger, G Katz, Y Adi, J Keshet
LPAR 2020, 23rd, 2020
922020
Deepsafe: A data-driven approach for checking adversarial robustness in neural networks
D Gopinath, G Katz, CS Pasareanu, C Barrett
arXiv preprint arXiv:1710.00486, 2017
902017
Ground-Truth Adversarial Examples
N Carlini, G Katz, C Barrett, DL Dill
arXiv preprint arXiv:1709.10207v1, 2017
892017
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
852019
An SMT-based approach for verifying binarized neural networks
G Amir, H Wu, C Barrett, G Katz
Tools and Algorithms for the Construction and Analysis of Systems: 27th …, 2021
762021
Dillig I Tasiran S et al
G Katz
The marabou framework for verification and analysis of deep neural networks …, 2019
682019
Verifying learning-augmented systems
T Eliyahu, Y Kazak, G Katz, M Schapira
Proceedings of the 2021 ACM SIGCOMM 2021 Conference, 305-318, 2021
652021
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
642020
Reluplex: a calculus for reasoning about deep neural networks
G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer
Formal Methods in System Design 60 (1), 87-116, 2022
572022
Verifying recurrent neural networks using invariant inference
Y Jacoby, C Barrett, G Katz
Automated Technology for Verification and Analysis: 18th International …, 2020
572020
Towards scalable verification of deep reinforcement learning
G Amir, M Schapira, G Katz
2021 formal methods in computer aided design (FMCAD), 193-203, 2021
542021
Neural network robustness as a verification property: a principled case study
M Casadio, E Komendantskaya, ML Daggitt, W Kokke, G Katz, G Amir, ...
International conference on computer aided verification, 219-231, 2022
482022
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
482018
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