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 | 2218 | 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 | 604 | 2019 |

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 | 151 | 2017 |

An abstraction-based framework for neural network verification YY Elboher, J Gottschlich, G Katz Computer Aided Verification: 32nd International Conference, CAV 2020, Los …, 2020 | 141 | 2020 |

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 | 128 | 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 | 115 | 2017 |

Provably minimally-distorted adversarial examples N Carlini, G Katz, C Barrett, DL Dill arXiv preprint arXiv:1709.10207, 2017 | 114 | 2017 |

Ground-Truth Adversarial Examples N Carlini, G Katz, C Barrett, DL Dill arXiv preprint arXiv:1709.10207v1, 2017 | 89 | 2017 |

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 | 88 | 2017 |

Minimal Modifications of Deep Neural Networks using Verification. B Goldberger, G Katz, Y Adi, J Keshet LPAR 2020, 23rd, 2020 | 81 | 2020 |

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 | 78 | 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 | 67 | 2021 |

Dillig I Tasiran S et al G Katz The marabou framework for verification and analysis of deep neural networks …, 2019 | 63 | 2019 |

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 | 62 | 2020 |

Verifying learning-augmented systems T Eliyahu, Y Kazak, G Katz, M Schapira Proceedings of the 2021 ACM SIGCOMM 2021 Conference, 305-318, 2021 | 53 | 2021 |

Verifying recurrent neural networks using invariant inference Y Jacoby, C Barrett, G Katz Automated Technology for Verification and Analysis: 18th International …, 2020 | 53 | 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 | 52 | 2022 |

Towards scalable verification of deep reinforcement learning G Amir, M Schapira, G Katz 2021 formal methods in computer aided design (FMCAD), 193-203, 2021 | 47 | 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 | 46 | 2018 |

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 | 43 | 2017 |