Safety verification of deep neural networks X Huang, M Kwiatkowska, S Wang, M Wu International conference on computer aided verification, 3-29, 2017 | 1196 | 2017 |
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability X Huang, D Kroening, W Ruan, J Sharp, Y Sun, E Thamo, M Wu, X Yi Computer Science Review 37, 100270, 2020 | 575 | 2020 |
Concolic testing for deep neural networks Y Sun, M Wu, W Ruan, X Huang, M Kwiatkowska, D Kroening Proceedings of the 33rd ACM/IEEE International Conference on Automated …, 2018 | 362 | 2018 |
A game-based approximate verification of deep neural networks with provable guarantees M Wu, M Wicker, W Ruan, X Huang, M Kwiatkowska Theoretical Computer Science 807, 298-329, 2020 | 137 | 2020 |
Global robustness evaluation of deep neural networks with provable guarantees for the Hamming distance W Ruan, M Wu, Y Sun, X Huang, D Kroening, M Kwiatkowska International Joint Conference on Artificial Intelligence, 2019 | 114 | 2019 |
Safety and trustworthiness of deep neural networks: A survey X Huang, D Kroening, M Kwiatkowska, W Ruan, Y Sun, E Thamo, M Wu, ... arXiv preprint arXiv:1812.08342, 151, 2018 | 55 | 2018 |
Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles M Wu, T Louw, M Lahijanian, W Ruan, X Huang, N Merat, M Kwiatkowska 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 40 | 2019 |
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Norm W Ruan, M Wu, Y Sun, X Huang, D Kroening, M Kwiatkowska arXiv preprint arXiv:1804.05805, 2018 | 38 | 2018 |
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 | 35 | 2024 |
Robustness Guarantees for Deep Neural Networks on Videos M Wu, M Kwiatkowska 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 | 33 | 2020 |
A survey of safety and trustworthiness of deep neural networks: verification, testing, adversarial attack and defence, and interpretability. Comput. Sci. Rev. 37, 100270 (2020) X Huang, D Kroening, W Ruan, J Sharp, Y Sun, E Thamo, M Wu, X Yi | 26 | 2020 |
Verix: Towards verified explainability of deep neural networks M Wu, H Wu, C Barrett Advances in Neural Information Processing Systems 36, 22247-22268, 2023 | 24 | 2023 |
A survey of safety and trustworthiness of deep neural networks X Huang, D Kroening, W Ruan, J Sharp, Y Sun, E Thamo, M Wu, X Yi arXiv preprint arXiv:1812.08342, 2018 | 23 | 2018 |
Assessing Robustness of Text Classification through Maximal Safe Radius Computation E La Malfa, M Wu, L Laurenti, B Wang, A Hartshorn, M Kwiatkowska Findings of the Association for Computational Linguistics: EMNLP 2020, 2949-2968, 2020 | 21 | 2020 |
Full Poincaré polarimetry enabled through physical inference C He, J Lin, J Chang, J Antonello, B Dai, J Wang, J Cui, J Qi, M Wu, ... Optica 9 (10), 1109-1114, 2022 | 17* | 2022 |
Towards efficient verification of quantized neural networks P Huang, H Wu, Y Yang, I Daukantas, M Wu, Y Zhang, C Barrett Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21152 …, 2024 | 16 | 2024 |
Convex bounds on the softmax function with applications to robustness verification D Wei, H Wu, M Wu, PY Chen, C Barrett, E Farchi International Conference on Artificial Intelligence and Statistics, 6853-6878, 2023 | 10 | 2023 |
Robustness Evaluation of Deep Neural Networks with Provable Guarantees M Wu University of Oxford, 2020 | 3 | 2020 |
Policy-specific abstraction predicate selection in neural policy safety verification M Vinzent, M Wu, H Wu, J Hoffmann Proc. 2nd Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS …, 2023 | 2 | 2023 |
Better Verified Explanations with Applications to Incorrectness and Out-of-Distribution Detection M Wu, X Li, H Wu, C Barrett arXiv preprint arXiv:2409.03060, 2024 | 1 | 2024 |