| 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 |
| 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 |
| 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 | 42 | 2022 |
| 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 | 27 | 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 | 20* | 2023 |
| Verification-Aided Deep Ensemble Selection. G Amir, T Zelazny, G Katz, M Schapira FMCAD, 27-37, 2022 | 18 | 2022 |
| Constrained reinforcement learning for robotics via scenario-based programming D Corsi, R Yerushalmi, G Amir, A Farinelli, D Harel, G Katz arXiv preprint arXiv:2206.09603, 2022 | 17 | 2022 |
| Verifying Generalization in Deep Learning International Conference on Computer Aided Verification (CAV), 438-455, 2023 | 13* | 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 | 12 | 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 | 11* | 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 | 9 | 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, ... arXiv preprint arXiv:2401.14461, 2024 | 8 | 2024 |
| Analyzing Adversarial Inputs in Deep Reinforcement Learning D Corsi, G Amir, G Katz, A Farinelli arXiv preprint arXiv:2402.05284, 2024 | 6 | 2024 |
| Towards Scalable Verification of Deep Reinforcement Learning. In 2021 Formal Methods in Computer-Aided Design (FMCAD). 193ś203 G Amir, M Schapira, G Katz | 6 | 2021 |
| 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 | 5 | 2023 |
| Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates U Mandal, G Amir, H Wu, I Daukantas, FL Newell, UJ Ravaioli, B Meng, ... arXiv preprint arXiv:2405.14058, 2024 | 4 | 2024 |
| Shield Synthesis for LTL Modulo Theories A Rodriguez, G Amir, D Corsi, C Sanchez, G Katz arXiv preprint arXiv:2406.04184, 2024 | 3 | 2024 |
| Verification-Guided Shielding for Deep Reinforcement Learning D Corsi, G Amir, A Rodriguez, C Sanchez, G Katz, R Fox arXiv preprint arXiv:2406.06507, 2024 | 2 | 2024 |
| Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment G Amir, A Prusak, T Reiss, N Zabari, DG Feitelson 2021 IEEE/ACM 8th International Workshop on Software Engineering Research …, 2021 | 2 | 2021 |
| Diffusion-based handedness classification for touch-based input A Hakim, GD Amir, A Slobodkin US Patent 11,537,239, 2022 | 1 | 2022 |
Guy KatzThe Hebrew University of Jerusalem在 cs.huji.ac.il 的电子邮件经过验证
