Katz Guy

Research output

Research output per year 2011 2016 2021 2022 2023 2024 2024

• 55 Conference contribution
• 9 Article
• 7 Conference article
• 2 Editorial
• 1 More
  • 1 Abstract

Research output per year

Research output per year

Search results

• 2024

  DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace

  Katz, G., Levy, N., Refaeli, I. & Yerushalmi, R., 2024, DASC 2024 - Digital Avionics Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 8 p. (AIAA/IEEE Digital Avionics Systems Conference - Proceedings).

  The Hebrew University of Jerusalem, Weizmann Institute of Science

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

• gRoMA: A Tool for Measuring the Global Robustness of Deep Neural Networks

  Levy, N., Yerushalmi, R. & Katz, G., 2024, Bridging the Gap Between AI and Reality - 1st International Conference, AISoLA 2023, Proceedings. Steffen, B. (ed.). Springer Science and Business Media B.V., p. 160-170 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14380 LNCS).

  Weizmann Institute of Science, The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Deep neural networks 100%
  • Neural Networks 96%
  • Robustness 86%
  • Output 25%
  • Safety-critical Systems 21%

• Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation

  Amir, G., Bassan, S. & Katz, G., 16 Oct 2024, ECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings. Endriss, U., Melo, F. S., Bach, K., Bugarin-Diz, A., Alonso-Moral, J. M., Barro, S. & Heintz, F. (eds.). p. 818-825 8 p. (Frontiers in Artificial Intelligence and Applications; vol. 392).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

• Learning DNN Abstractions using Gradient Descent

  Mukhopadhyay, D., Siddiqui, S., Karmarkar, H., Madhukar, K. & Katz, G., 27 Oct 2024, Proceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024. p. 2299-2303 5 p. (Proceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

• Marabou 2.0: A Versatile Formal Analyzer of Neural Networks

  Wu, H., Isac, O., Zeljić, A., Tagomori, T., Daggitt, M., Kokke, W., Refaeli, I., Amir, G., Julian, K., Bassan, S., Huang, P., Lahav, O., Wu, M., Zhang, M., Komendantskaya, E., Katz, G. & Barrett, C., 2024, Computer Aided Verification - 36th International Conference, CAV 2024, Proceedings. Gurfinkel, A. & Ganesh, V. (eds.). Springer Science and Business Media Deutschland GmbH, p. 249-264 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14682 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

• On Augmenting Scenario-Based Modeling with Generative AI

  Harel, D., Katz, G., Marron, A. & Szekely, S., 2024, Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering. Domínguez Mayo, F. J., Pires, L. F. & Seidewitz, E. (eds.). p. 235-246 12 p. (International Conference on Model-Driven Engineering and Software Development; vol. 1).

  Weizmann Institute of Science, The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Large scale systems 100%
  • Inspection 88%
  • Engineers 81%

• Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing

  Elboher, Y., Elsaleh, R., Isac, O., Ducoffe, M., Galametz, A., Povéda, G., Boumazouza, R., Cohen, N. & Katz, G., 2024, DASC 2024 - Digital Avionics Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., (AIAA/IEEE Digital Avionics Systems Conference - Proceedings).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

• Safe and Reliable Training of Learning-Based Aerospace Controllers

  Mandal, U., Amir, G., Wu, H., Daukantas, I., Newell, F. L., Ravaioli, U., Meng, B., Durling, M., Hobbs, K., Ganai, M., Shim, T., Katz, G. & Barrett, C., 2024, DASC 2024 - Digital Avionics Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., (AIAA/IEEE Digital Avionics Systems Conference - Proceedings).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

• Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training

  Tian, J., Zhi, D., Liu, S., Wang, P., Katz, G. & Zhang, M., 2024, Verification, Model Checking, and Abstract Interpretation - 25th International Conference, VMCAI 2024, Proceedings. Dimitrova, R., Lahav, O. & Wolff, S. (eds.). Springer Science and Business Media Deutschland GmbH, p. 73-97 25 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14500 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Reachability Analysis 100%
  • Abstraction 74%
  • Training 72%
  • Deep neural networks 66%
  • Neural Networks 64%
• 2023

  DelBugV: Delta-Debugging Neural Network Verifiers

  Elsaleh, R. & Katz, G., 2023, Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design, FMCAD 2023. Nadel, A., Rozier, K. Y., Hunt, W. A. & Weissenbacher, G. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 34-43 10 p. (Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design, FMCAD 2023).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Deep neural networks 100%
  • Neural networks 62%
  • Engines 7%

• DNN Verification, Reachability, and the Exponential Function Problem

  Isac, O., Zohar, Y., Barrett, C. & Katz, G., Sep 2023, 34th International Conference on Concurrency Theory, CONCUR 2023. Perez, G. A. & Raskin, J-F. (eds.). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, p. 26:1-26:18 18 p. 26. (Leibniz International Proceedings in Informatics, LIPIcs; vol. 279).

  Bar-Ilan University, The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Exponential functions 100%
  • Deep neural networks 79%
  • Chemical activation 32%
  • Computability and decidability 12%
  • Opacity 11%

• Enhancing Deep Learning with Scenario-Based Override Rules: A Case Study.

  Ashrov, A. & Katz, G., 2023, Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering, MODELSWARD 2023. p. 253-268 16 p.

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

• Formally Explaining Neural Networks within Reactive Systems

  Bassan, S., Amir, G., Corsi, D., Refaeli, I. & Katz, G., 2023, Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design, FMCAD 2023. Nadel, A., Rozier, K. Y., Hunt, W. A. & Weissenbacher, G. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 10-22 13 p. (Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design, FMCAD 2023).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Deep neural networks 100%
  • Neural networks 62%
  • Formal methods 23%
  • Navigation 15%
  • Controllers 11%

• OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks

  Guo, X., Zhou, Z., Zhang, Y., Katz, G. & Zhang, M., 2023, Tools and Algorithms for the Construction and Analysis of Systems - 29th International Conference, TACAS 2023, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022, Proceedings. Sankaranarayanan, S. & Sharygina, N. (eds.). Springer Science and Business Media Deutschland GmbH, p. 208-226 19 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13993 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Occlusion 100%
  • Surface mount technology 78%
  • Deep neural networks 65%
  • Neural Networks 63%
  • Robustness 56%

• RoMA: A Method for Neural Network Robustness Measurement and Assessment

  Levy, N. & Katz, G., 2023, Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A. & Jatowt, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 92-105 14 p. (Communications in Computer and Information Science; vol. 1791 CCIS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Neural Networks 100%
  • Robustness 88%
  • Neural networks 64%
  • Neural Network Model 52%
  • Safety-critical Systems 40%

• Towards a Certified Proof Checker for Deep Neural Network Verification

  Desmartin, R., Isac, O., Passmore, G., Stark, K., Komendantskaya, E. & Katz, G., 2023, Logic-Based Program Synthesis and Transformation - 33rd International Symposium, LOPSTR 2023, Proceedings. Glück, R. & Kafle, B. (eds.). Springer Science and Business Media Deutschland GmbH, p. 198-209 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14330 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Formal Verification 100%
  • Deep neural networks 99%
  • Neural Networks 96%
  • Convergence of numerical methods 85%
  • Formal verification 80%

• Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks

  Bassan, S. & Katz, G., 2023, Tools and Algorithms for the Construction and Analysis of Systems - 29th International Conference, TACAS 2023, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022, Proceedings. Sankaranarayanan, S. & Sharygina, N. (eds.). Springer Science and Business Media Deutschland GmbH, p. 187-207 21 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13993 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Deep neural networks 100%
  • Neural Networks 96%
  • Neural networks 62%
  • Scalability 53%
  • Safety-critical Systems 38%

• veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System

  Amir, G., Freund, Z., Katz, G., Mandelbaum, E. & Refaeli, I., 2023, Formal Methods - 25th International Symposium, FM 2023, Proceedings. Chechik, M., Katoen, J-P. & Leucker, M. (eds.). Springer Science and Business Media Deutschland GmbH, p. 648-656 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14000 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Learning 100%
  • Deep neural networks 53%
  • Safety-critical Systems 51%
  • Collaboration 34%
  • Industry 28%

• Verifying Generalization in Deep Learning

  Amir, G., Maayan, O., Zelazny, T., Katz, G. & Schapira, M., 2023, Computer Aided Verification - 35th International Conference, CAV 2023, Proceedings. Enea, C. & Lal, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 438-455 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13965 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Deep neural networks 100%
  • Neural Networks 96%
  • Learning 93%
  • Deep learning 92%
  • Generalization 51%

• Verifying Learning-Based Robotic Navigation Systems

  Amir, G., Corsi, D., Yerushalmi, R., Marzari, L., Harel, D., Farinelli, A. & Katz, G., 2023, Tools and Algorithms for the Construction and Analysis of Systems - 29th International Conference, TACAS 2023, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022, Proceedings. Sankaranarayanan, S. & Sharygina, N. (eds.). Springer Science and Business Media B.V., p. 607-627 21 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13993 LNCS).

  Weizmann Institute of Science, The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Navigation System 100%
  • Robotics 71%
  • Navigation systems 58%
  • Policy 57%
  • Learning 53%
• 2022

  An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks

  Ostrovsky, M., Barrett, C. & Katz, G., 2022, Automated Technology for Verification and Analysis - 20th International Symposium, ATVA 2022, Proceedings. Bouajjani, A., Holík, L. & Wu, Z. (eds.). Springer Science and Business Media Deutschland GmbH, p. 391-396 6 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13505 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Abstraction 100%
  • Neural Networks 86%
  • Convolutional neural networks 83%
  • Refinement 80%
  • Engine 58%

• Efficient Neural Network Analysis with Sum-of-Infeasibilities

  Wu, H., Zeljić, A., Katz, G. & Barrett, C., 2022, Tools and Algorithms for the Construction and Analysis of Systems - 28th International Conference, TACAS 2022, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022, Proceedings. Fisman, D. & Rosu, G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 143-163 21 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13243 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Infeasibility 100%
  • Network Analysis 95%
  • Electric network analysis 73%
  • Activation Function 68%
  • Neural Networks 68%

• Minimal Multi-Layer Modifications of Deep Neural Networks

  Refaeli, I. & Katz, G., 2022, Software Verification and Formal Methods for ML-Enabled Autonomous Systems - 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Proceedings. Isac, O., Katz, G., Ivanov, R., Narodytska, N. & Nenzi, L. (eds.). Springer Science and Business Media Deutschland GmbH, p. 46-66 21 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13466 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Multilayer 100%
  • Deep neural networks 78%
  • Neural Networks 76%
  • Collision Avoidance 17%
  • Engine 12%

• Neural Network Robustness as a Verification Property: A Principled Case Study

  Casadio, M., Komendantskaya, E., Daggitt, M. L., Kokke, W., Katz, G., Amir, G. & Refaeli, I., 2022, Computer Aided Verification - 34th International Conference, CAV 2022, Proceedings. Shoham, S. & Vizel, Y. (eds.). Springer Science and Business Media Deutschland GmbH, p. 219-231 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13371 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Neural Networks 100%
  • Robustness 88%
  • Neural networks 64%
  • Empirical Analysis 49%
  • Susceptibility 44%

• Neural Network Verification Using Residual Reasoning

  Elboher, Y. Y., Cohen, E. & Katz, G., 2022, Software Engineering and Formal Methods - 20th International Conference, SEFM 2022, Proceedings. Schlingloff, B-H. & Chai, M. (eds.). Springer Science and Business Media Deutschland GmbH, p. 173-189 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13550 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Reasoning 100%
  • Neural Networks 98%
  • Neural networks 63%
  • Scalability 42%
  • Complex networks 26%

• Neural Network Verification with Proof Production

  Isac, O., Barrett, C., Zhang, M. & Katz, G., 2022, Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design, FMCAD 2022. Griggio, A. & Rungta, N. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 38-48 11 p. (Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design, FMCAD 2022).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Deep neural networks 100%
  • Neural Networks 96%
  • Neural networks 62%
  • Safety-critical Systems 55%
  • Collision Avoidance 24%

• On Optimizing Back-Substitution Methods for Neural Network Verification

  Zelazny, T., Wu, H., Barrett, C. & Katz, G., 2022, Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design, FMCAD 2022. Griggio, A. & Rungta, N. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 17-26 10 p. (Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design, FMCAD 2022).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Substitution reactions 100%
  • Substitution 95%
  • Neural Networks 95%
  • Neural networks 61%
  • Propagation 36%

• Verification-Aided Deep Ensemble Selection

  Amir, G., Zelazny, T., Katz, G. & Schapira, M., 2022, Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design, FMCAD 2022. Griggio, A. & Rungta, N. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 27-37 11 p. (Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design, FMCAD 2022).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Ensemble 100%
  • Deep neural networks 97%
  • Neural Networks 94%
  • Misclassification 32%
  • Neural Network Ensemble 27%
• 2021

  An SMT-Based Approach for Verifying Binarized Neural Networks

  Amir, G., Wu, H., Barrett, C. & Katz, G., 2021, Tools and Algorithms for the Construction and Analysis of Systems - 27th International Conference, TACAS 2021 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. Groote, J. F. & Larsen, K. G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 203-222 20 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12652 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Surface mount technology 100%
  • Neural Networks 80%
  • Neural networks 51%
  • Energy Efficient 17%
  • Evaluate 17%

• Augmenting Deep Neural Networks with Scenario-Based Guard Rules

  Katz, G., 2021, Model-Driven Engineering and Software Development - 8th International Conference, MODELSWARD 2020, Revised Selected Papers. Hammoudi, S., Pires, L. F. & Selić, B. (eds.). Springer Science and Business Media Deutschland GmbH, p. 147-172 26 p. (Communications in Computer and Information Science; vol. 1361).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Deep neural networks 100%
  • Neural Networks 96%
  • Scenarios 94%
  • Paradigm 60%
  • Modeling 40%

• Pruning and Slicing Neural Networks using Formal Verification

  Lahav, O. & Katz, G., 2021, Proceedings of the 21st Formal Methods in Computer-Aided Design, FMCAD 2021. Piskac, R., Whalen, M. W., Hunt, W. A. & Weissenbacher, G. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 183-192 10 p. (Proceedings of the 21st Formal Methods in Computer-Aided Design, FMCAD 2021).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Formal verification 100%
  • Deep neural networks 82%
  • Neural networks 51%
  • Topology 16%
  • Neurons 11%

• Towards combining deep learning, verification, and scenario-based programming

  Katz, G. & Elyasaf, A., 18 May 2021, Proceedings of the 1st International Workshop on Verification of Autonomous and Robotic Systems, VARS 2021. 3459631. (Proceedings of the 1st International Workshop on Verification of Autonomous and Robotic Systems, VARS 2021).

  The Hebrew University of Jerusalem, Ben-Gurion University of the Negev

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Autonomous vehicles 100%
  • Deep neural networks 91%
  • Deep learning 83%
  • Engineers 65%
  • Controllers 50%

• Towards repairing scenario-Based models with rich events

  Katz, G., 2021, MODELSWARD 2021 - Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development. Hammoudi, S., Pires, L. F., Seidewitz, E. & Soley, R. (eds.). p. 362-372 11 p. (MODELSWARD 2021 - Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

• Towards Scalable Verification of Deep Reinforcement Learning

  Amir, G., Schapira, M. & Katz, G., 2021, Proceedings of the 21st Formal Methods in Computer-Aided Design, FMCAD 2021. Piskac, R., Whalen, M. W., Hunt, W. A. & Weissenbacher, G. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 193-203 11 p. (Proceedings of the 21st Formal Methods in Computer-Aided Design, FMCAD 2021).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Reinforcement learning 100%
  • Learning systems 76%
  • Deep neural networks 48%
  • Formal verification 29%
  • Telecommunication networks 21%

• Verifying learning-augmented systems

  Eliyahu, T., Kazak, Y., Katz, G. & Schapira, M., 9 Aug 2021, SIGCOMM 2021 - Proceedings of the ACM SIGCOMM 2021 Conference. p. 305-318 14 p. (SIGCOMM 2021 - Proceedings of the ACM SIGCOMM 2021 Conference).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Reinforcement learning 100%
  • Formal verification 29%
  • Video streaming 27%
  • Model checking 26%
  • Deep neural networks 24%
• 2020

  An Abstraction-Based Framework for Neural Network Verification

  Elboher, Y. Y., Gottschlich, J. & Katz, G., 2020, Computer Aided Verification - 32nd International Conference, CAV 2020, Proceedings. Lahiri, S. K. & Wang, C. (eds.). p. 43-65 23 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12224 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Abstraction 100%
  • Neural Networks 86%
  • Neural networks 55%
  • Framework 52%
  • Approximation 37%

• Guarded Deep Learning using Scenario-based Modeling

  Katz, G., 2020, MODELSWARD 2020 - Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development. Selić, B. (ed.). p. 126-136 11 p. (International Conference on Model-Driven Engineering and Software Development).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

• Parallelization Techniques for Verifying Neural Networks

  Wu, H., Ozdemir, A., Zeljic, A., Julian, K., Irfan, A., Gopinath, D., Fouladi, S., Katz, G., Pasareanu, C. & Barrett, C., 21 Sep 2020, Proceedings of the 20th Conference on Formal Methods in Computer-Aided Design, FMCAD 2020. Ivrii, A., Strichman, O., Hunt, W. A. & Weissenbacher, G. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 128-137 10 p. 9283631. (Proceedings of the 20th Conference on Formal Methods in Computer-Aided Design, FMCAD 2020).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Parallelization 100%
  • Neural Networks 75%
  • Heuristics 69%
  • Neural networks 48%
  • Partitioning 40%

• Simplifying Neural Networks Using Formal Verification

  Gokulanathan, S., Feldsher, A., Malca, A., Barrett, C. & Katz, G., 2020, NASA Formal Methods - 12th International Symposium, NFM 2020, Proceedings. Lee, R., Jha, S. & Mavridou, A. (eds.). p. 85-93 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12229 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Formal Verification 100%
  • Engine 86%
  • Formal verification 80%
  • Neural Networks 64%
  • Simplification 45%

• Verifying Recurrent Neural Networks Using Invariant Inference

  Jacoby, Y., Barrett, C. & Katz, G., 2020, Automated Technology for Verification and Analysis - 18th International Symposium, ATVA 2020, Proceedings. Hung, D. V. & Sokolsky, O. (eds.). Springer Science and Business Media Deutschland GmbH, p. 57-74 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12302 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Recurrent Neural Networks 100%
  • Recurrent neural networks 80%
  • Neural Networks 73%
  • Recurrent Networks 62%
  • Speech Recognition 59%
• 2019

  On-the-FlyConstruction of Composite Events in Scenario-Based Modeling using Constraint Solvers

  Katz, G., Marron, A., Sadon, A. & Weiss, G., 1 Jan 2019, MODELSWARD 2019 - Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development. Selić, B. (ed.). p. 141-154 14 p. (International Conference on Model-Driven Engineering and Software Development).

  The Hebrew University of Jerusalem, Ben-Gurion University of the Negev, Weizmann Institute of Science

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Surface mount technology 100%
  • Composite materials 49%

• On-the-fly construction of composite events in scenario-based modeling using constraint solvers

  Katz, G., Marron, A., Sadon, A. & Weiss, G., 1 Jan 2019, MODELSWARD 2019 - Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development. Hammoudi, S., Pires, L. F. & Selic, B. (eds.). p. 143-156 14 p. (MODELSWARD 2019 - Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development).

  The Hebrew University of Jerusalem, Ben-Gurion University of the Negev, Weizmann Institute of Science

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Surface mount technology 100%
  • Composite materials 49%

• The Marabou Framework for Verification and Analysis of Deep Neural Networks

  Katz, G., Huang, D. A., Ibeling, D., Julian, K., Lazarus, C., Lim, R., Shah, P., Thakoor, S., Wu, H., Zeljić, A., Dill, D. L., Kochenderfer, M. J. & Barrett, C., 2019, Computer Aided Verification - 31st International Conference, CAV 2019, Proceedings. Dillig, I. & Tasiran, S. (eds.). Springer Verlag, p. 443-452 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11561 LNCS).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Deep neural networks 100%
  • Neural Networks 96%
  • Query 67%
  • Constraint satisfaction problems 66%
  • Surface mount technology 60%

• Verifying deep-rl-driven systems

  Kazak, Y., Barrett, C., Katz, G. & Schapira, M., 14 Aug 2019, NetAI 2019 - Proceedings of the 2019 ACM SIGCOMM Workshop on Network Meets AI and ML, Part of SIGCOMM 2019. p. 83-89 7 p. (NetAI 2019 - Proceedings of the 2019 ACM SIGCOMM Workshop on Network Meets AI and ML, Part of SIGCOMM 2019).

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Reinforcement learning 100%
  • Learning systems 51%
  • Video streaming 27%
  • Decision making 27%
  • Formal verification 14%
• 2018

  DeepSafe: A Data-Driven Approach for Assessing Robustness of Neural Networks

  Gopinath, D., Katz, G., Păsăreanu, C. S. & Barrett, C., 2018, Automated Technology for Verification and Analysis - 16th International Symposium, ATVA 2018, Proceedings. Wang, C. & Lahiri, S. K. (eds.). Springer Verlag, p. 3-19 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11138 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Data-driven 100%
  • Neural Networks 82%
  • Robustness 73%
  • Neural networks 53%
  • Perturbation 45%
• 2017

  Distributing scenario-based models: A replicate-and-project approach

  Steinberg, S., Greenyer, J., Gritzner, D., Harel, D., Katz, G. & Marron, A., 2017, MODELSWARD 2017 - Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development. Pires, L. F., Hammoudi, S. & Selic, B. (eds.). p. 182-195 14 p. (MODELSWARD 2017 - Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development; vol. 2017-January).

  Weizmann Institute of Science

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Synchronization 100%
  • Specifications 80%

• Lazy proofs for DPLL(T)-based SMT solvers

  Katz, G., Barrett, C., Tinelli, C., Reynolds, A. & Hadarean, L., 24 Mar 2017, Proceedings of the 16th Conference on Formal Methods in Computer-Aided Design, FMCAD 2016. Piskac, R. & Talupur, M. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 93-100 8 p. 7886666. (Proceedings of the 16th Conference on Formal Methods in Computer-Aided Design, FMCAD 2016).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Surface mount technology 100%

• Reluplex: An efficient smt solver for verifying deep neural networks

  Katz, G., Barrett, C., Dill, D. L., Julian, K. & Kochenderfer, M. J., 2017, Computer Aided Verification - 29th International Conference, CAV 2017, Proceedings. Kuncak, V. & Majumdar, R. (eds.). Springer Verlag, p. 97-117 21 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10426 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Deep neural networks 100%
  • Neural Networks 96%
  • Neural networks 41%
  • Safety-critical Systems 38%
  • Collision Avoidance 34%

• Smtcoq: A plug-in for integrating smt solvers into coq

  Ekici, B., Mebsout, A., Tinelli, C., Keller, C., Katz, G., Reynolds, A. & Barrett, C., 2017, Computer Aided Verification - 29th International Conference, CAV 2017, Proceedings. Kuncak, V. & Majumdar, R. (eds.). Springer Verlag, p. 126-133 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10427 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Plug-in 100%
  • Surface mount technology 92%
  • Certificate 65%
  • Automation 32%
  • Propositional Logic 29%
• 2016

  Scenario-Based Modeling and Synthesis for Reactive Systems with Dynamic System Structure in ScenarioTools.

  Greenyer, J., Gritzner, D., Katz, G. & Marron, A., 2016, Proceedings of the MoDELS 2016 Demo and Poster Sessions co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MoDELS 2016): CEUR Workshop Proceedings 1725, CEUR-WS.org 2016. p. 16-23 8 p.

  The Hebrew University of Jerusalem

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review