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Guy Katz
Guy Katz
Bestätigte E-Mail-Adresse bei cs.huji.ac.il - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
21912017
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
5982019
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
1492017
An abstraction-based framework for neural network verification
YY Elboher, J Gottschlich, G Katz
Computer Aided Verification: 32nd International Conference, CAV 2020, Los …, 2020
1392020
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
1282018
Provably minimally-distorted adversarial examples
N Carlini, G Katz, C Barrett, DL Dill
arXiv preprint arXiv:1709.10207, 2017
1142017
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
1112017
Ground-Truth Adversarial Examples
N Carlini, G Katz, C Barrett, DL Dill
arXiv preprint arXiv:1709.10207v1, 2017
892017
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
862017
Minimal Modifications of Deep Neural Networks using Verification.
B Goldberger, G Katz, Y Adi, J Keshet
LPAR 2020, 23rd, 2020
812020
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
782019
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
672021
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
592020
Verifying learning-augmented systems
T Eliyahu, Y Kazak, G Katz, M Schapira
Proceedings of the 2021 ACM SIGCOMM 2021 Conference, 305-318, 2021
532021
Verifying recurrent neural networks using invariant inference
Y Jacoby, C Barrett, G Katz
Automated Technology for Verification and Analysis: 18th International …, 2020
532020
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
512022
Towards scalable verification of deep reinforcement learning
G Amir, M Schapira, G Katz
2021 formal methods in computer aided design (FMCAD), 193-203, 2021
472021
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
452018
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
432017
Simplifying neural networks using formal verification
S Gokulanathan, A Feldsher, A Malca, C Barrett, G Katz
NASA Formal Methods: 12th International Symposium, NFM 2020, Moffett Field …, 2020
422020
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