| 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 | 2334 | 2017 |
| 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 | 660 | 2019 |
| An abstraction-based framework for neural network verification YY Elboher, J Gottschlich, G Katz Computer Aided Verification: 32nd International Conference, CAV 2020, Los …, 2020 | 152 | 2020 |
| 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 | 152 | 2017 |
| 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 | 131 | 2018 |
| 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 | 119 | 2017 |
| Provably minimally-distorted adversarial examples N Carlini, G Katz, C Barrett, DL Dill arXiv preprint arXiv:1709.10207, 2017 | 118 | 2017 |
| Minimal Modifications of Deep Neural Networks using Verification. B Goldberger, G Katz, Y Adi, J Keshet LPAR 2020, 23rd, 2020 | 91 | 2020 |
| 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 | 90 | 2017 |
| Ground-Truth Adversarial Examples N Carlini, G Katz, C Barrett, DL Dill arXiv preprint arXiv:1709.10207v1, 2017 | 89 | 2017 |
| 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 | 85 | 2019 |
| 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 | 74 | 2021 |
| Dillig I Tasiran S et al G Katz The marabou framework for verification and analysis of deep neural networks …, 2019 | 68 | 2019 |
| Verifying learning-augmented systems T Eliyahu, Y Kazak, G Katz, M Schapira Proceedings of the 2021 ACM SIGCOMM 2021 Conference, 305-318, 2021 | 64 | 2021 |
| 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 | 64 | 2020 |
| 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 | 57 | 2022 |
| Verifying recurrent neural networks using invariant inference Y Jacoby, C Barrett, G Katz Automated Technology for Verification and Analysis: 18th International …, 2020 | 57 | 2020 |
| Towards scalable verification of deep reinforcement learning G Amir, M Schapira, G Katz 2021 formal methods in computer aided design (FMCAD), 193-203, 2021 | 53 | 2021 |
| 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 | 48 | 2018 |
| 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 | 46 | 2022 |
