| An abstract domain for certifying neural networks G Singh, T Gehr, M Püschel, M Vechev Proceedings of the ACM on Programming Languages 3 (POPL), 1-30, 2019 | 465 | 2019 |
| Fast and effective robustness certification G Singh, T Gehr, M Mirman, M Püschel, M Vechev Advances in neural information processing systems 31, 2018 | 391 | 2018 |
| Boosting robustness certification of neural networks G Singh, T Gehr, M Püschel, M Vechev International conference on learning representations, 2019 | 157* | 2019 |
| Beyond the single neuron convex barrier for neural network certification G Singh, R Ganvir, M Püschel, M Vechev Advances in Neural Information Processing Systems 32, 15098-15109, 2019 | 123 | 2019 |
| Fast polyhedra abstract domain G Singh, M Püschel, M Vechev Proceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming …, 2017 | 95 | 2017 |
| Certifying geometric robustness of neural networks M Balunovic, M Baader, G Singh, T Gehr, M Vechev Advances in Neural Information Processing Systems 32, 2019 | 89 | 2019 |
| PRIMA: general and precise neural network certification via scalable convex hull approximations. MN Müller, G Makarchuk, G Singh, M Püschel, MT Vechev Proc. ACM Program. Lang. 6 (POPL), 1-33, 2022 | 45* | 2022 |
| Making Numerical Program Analysis Fast G Singh, M Püschel, M Vechev Programming Language Design and Implementation (PLDI) 50 (6), 303-313, 2015 | 44 | 2015 |
| Scaling polyhedral neural network verification on GPUs C Müller, F Serre, G Singh, M Püschel, M Vechev Proceedings of Machine Learning and Systems 3, 733-746, 2021 | 39* | 2021 |
| A practical construction for decomposing numerical abstract domains G Singh, M Püschel, M Vechev Proceedings of the ACM on Programming Languages 2 (POPL), 1-28, 2017 | 36 | 2017 |
| Fast numerical program analysis with reinforcement learning G Singh, M Püschel, M Vechev Computer Aided Verification: 30th International Conference, CAV 2018, Held …, 2018 | 33 | 2018 |
| Scalable Polyhedral Verification of Recurrent Neural Networks W Ryou, J Chen, M Balunovic, G Singh, A Dan, M Vechev International Conference on Computer Aided Verification, 225-248, 2021 | 25* | 2021 |
| A Provable Defense for Deep Residual Networks M Mirman, G Singh, M Vechev arXiv preprint arXiv:1903.12519, 2019 | 21 | 2019 |
| Learning fast and precise numerical analysis J He, G Singh, M Püschel, M Vechev Proceedings of the 41st ACM SIGPLAN Conference on Programming Language …, 2020 | 20 | 2020 |
| Robustness certification for point cloud models T Lorenz, A Ruoss, M Balunović, G Singh, M Vechev Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 15 | 2021 |
| Adversarial Attacks on Probabilistic Autoregressive Forecasting Models R Dang-Nhu, G Singh, P Bielik, M Vechev International Conference on Machine Learning, 2020 | 13 | 2020 |
| Organizational level responses to the COVID-19 outbreak: Challenges, strategies and framework for academic institutions J Biswakarma, D Rushworth, G Srivastava, G Singh, K Kang, S Das, ... Frontiers in Communication 6, 573585, 2021 | 9 | 2021 |
| A dual number abstraction for static analysis of Clarke Jacobians J Laurel, R Yang, G Singh, S Misailovic Proceedings of the ACM on Programming Languages 6 (POPL), 1-30, 2022 | 6 | 2022 |
| FIRE: enabling reciprocity for FDD MIMO systems Z Liu, G Singh, C Xu, D Vasisht Proceedings of the 27th Annual International Conference on Mobile Computing …, 2021 | 5 | 2021 |
| Scalable verification of GNN-based job schedulers H Wu, C Barrett, M Sharif, N Narodytska, G Singh Proceedings of the ACM on Programming Languages 6 (OOPSLA2), 1036-1065, 2022 | 2 | 2022 |
Martin VechevProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
