| AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation T Gehr, M Mirman, D Drachsler-Cohen, P Tsankov, S Chaudhuri, ... IEEE S&P, 2018 | 1127 | 2018 |
| Fast and effective robustness certification G Singh, T Gehr, M Mirman, M Püschel, M Vechev Advances in Neural Information Processing Systems (NeurIPS), 10802-10813, 2018 | 637 | 2018 |
| Differentiable Abstract Interpretation for Provably Robust Neural Networks M Mirman, T Gehr, M Vechev ICML, 2018 | 624 | 2018 |
| Online robustness training for deep reinforcement learning M Fischer, M Mirman, S Stalder, M Vechev arXiv preprint arXiv:1911.00887, 2019 | 48 | 2019 |
| Robustness certification with generative models M Mirman, A Hägele, P Bielik, T Gehr, M Vechev Proceedings of the 42nd ACM SIGPLAN International Conference on Programming …, 2021 | 32 | 2021 |
| Universal approximation with certified networks M Baader, M Mirman, M Vechev International Conference on Learning Representations (ICLR), 2020 | 30 | 2020 |
| A provable defense for deep residual networks M Mirman, G Singh, M Vechev arXiv preprint arXiv:1903.12519, 2019 | 25 | 2019 |
| ETH robustness analyzer for neural networks (ERAN), 2020 G Singh, J Maurer, C Müller, M Mirman, T Gehr, A Hoffmann, P Tsankov, ... URL https://github. com/eth-sri/eran, 2022 | 16* | 2022 |
| The fundamental limits of neural networks for interval certified robustness MB Mirman, M Baader, M Vechev | 12* | 2022 |
| Distilled agent DQN for provable adversarial robustness, 2018 M Mirman, M Fischer, M Vechev URL https://openreview. net/forum, 0 | 9 | |
| Ten simple rules for using large language models in science, version 1.0 GR Smith, C Bello, L Bialic-Murphy, E Clark, CS Delavaux, ... PLOS Computational Biology 20 (1), e1011767, 2024 | 6 | 2024 |
| Fast and effective robustness certification S Gagandeep, G Timon, M Matthew, P Markus, V Martin Advances in Neural Information Processing Systems 31, 2018 | 5 | 2018 |
| Training Neural Machines with Trace-Based Supervision M Mirman, D Dimitrov, P Djordjevich, T Gehr, M Vechev ICML, 2018 | 3 | 2018 |
| Performant proximity matching between sets of unstructured records M Mirman US Patent 11,995,109, 2024 | | 2024 |
| LLM Guided Inductive Inference for Solving Compositional Problems A Sodani, L Moos, M Mirman arXiv preprint arXiv:2309.11688, 2023 | | 2023 |
| Certified Deep Learning: Verification and Training M Mirman ETH Zurich, 2022 | | 2022 |
| Logic Programming and Type Inference with the Calculus of Constructions M Mirman Carnegie Mellon University Pittsburgh, PA, 2014 | | 2014 |
| Modes for Non Strict Functional Logic Languages M Mirman | | 2012 |
| Inversion of Quadratic Bézier Triangles GL Miller, M Mirman, T Phillips Fall Workshop on Computational Geometry, 2010 | | 2010 |
| ERAN User Manual G Singh, M Balunovic, A Ruoss, C Müller, J Maurer, A Hoffmann, ... | | |
Martin VechevFull Professor of Computer Science, ETH Zurich; Scientific Director, INSAIT;Adresse e-mail validée de inf.ethz.ch