| 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 | 85 | 2021 |
| 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 | 59 | 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 | 58 | 2021 |
| Verifying learning-based robotic navigation systems G Amir, D Corsi, R Yerushalmi, L Marzari, D Harel, A Farinelli, G Katz International Conference on Tools and Algorithms for the Construction and …, 2023 | 38 | 2023 |
| Micro and macroevolution of sea anemone venom phenotype EG Smith, JM Surm, J Macrander, A Simhi, G Amir, MY Sachkova, ... Nature Communications 14 (1), 249, 2023 | 28* | 2023 |
| Marabou 2.0: a versatile formal analyzer of neural networks H Wu, O Isac, A Zeljić, T Tagomori, M Daggitt, W Kokke, I Refaeli, G Amir, ... International Conference on Computer Aided Verification, 249-264, 2024 | 27 | 2024 |
| Constrained reinforcement learning for robotics via scenario-based programming D Corsi, R Yerushalmi, G Amir, A Farinelli, D Harel, G Katz The 31st International Conference on Neural Information Processing (ICONIP), 2024 | 23 | 2024 |
| Verification-aided deep ensemble selection G Amir, T Zelazny, G Katz, M Schapira FMCAD, 27-37, 2022 | 20 | 2022 |
| Verifying Generalization in Deep Learning International Conference on Computer Aided Verification (CAV), 438-455, 2023 | 18* | 2023 |
| veriFIRE: verifying an industrial, learning-based wildfire detection system G Amir, Z Freund, G Katz, E Mandelbaum, I Refaeli International Symposium on Formal Methods, 648-656, 2023 | 14 | 2023 |
| Scenario-Assisted Deep Reinforcement Learning AM Raz Yerushalmi, Guy Amir, Achiya Elyasaf, David Harel, Guy Katz MODELSWARD 2022: the 10th International Conference on Model-Driven …, 2022 | 14* | 2022 |
| Formally explaining neural networks within reactive systems S Bassan, G Amir, D Corsi, I Refaeli, G Katz 2023 Formal Methods in Computer-Aided Design (FMCAD), 1-13, 2023 | 13 | 2023 |
| Analyzing adversarial inputs in deep reinforcement learning D Corsi, G Amir, G Katz, A Farinelli arXiv preprint arXiv:2402.05284, 2024 | 10 | 2024 |
| Enhancing deep reinforcement learning with scenario-based modeling R Yerushalmi, G Amir, A Elyasaf, D Harel, G Katz, A Marron SN computer science 4 (2), 156, 2023 | 10 | 2023 |
| Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates 2024 Formal Methods in Computer Aided Design (FMCAD), 95-106, 2024 | 7* | 2024 |
| Verification-Guided Shielding for Deep Reinforcement Learning D Corsi, G Amir, A Rodriguez, C Sanchez, G Katz, R Fox The 1st Reinforcement Learning Conference (RLC), 2024 | 7 | 2024 |
| Shield Synthesis for LTL Modulo Theories A Rodriguez, G Amir, D Corsi, C Sanchez, G Katz AAAI Conference on Artificial Intelligence, 2025 | 5 | 2025 |
| Towards Scalable Verification of Deep Reinforcement Learning. In 2021 Formal Methods in Computer-Aided Design (FMCAD). 193ś203 G Amir, M Schapira, G Katz | 5* | 2021 |
| Local vs. Global Interpretability: A Computational Complexity Perspective S Bassan, G Amir, G Katz International Conference on Machine Learning (ICML), 2024, 2024 | 4 | 2024 |
| Safe and Reliable Training of Learning-Based Aerospace Controllers U Mandal, G Amir, H Wu, I Daukantas, FL Newell, U Ravaioli, B Meng, ... 43rd Digital Avionics Systems Conference (DASC), 2024 | 3 | 2024 |
Guy KatzThe Hebrew University of JerusalemVerifisert e-postadresse på cs.huji.ac.il