Safety verification of deep neural networks X Huang, M Kwiatkowska, S Wang, M Wu
Computer Aided Verification: 29th International Conference, CAV 2017 …, 2017
1066 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
519 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
332 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
126 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
99 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
35 2018 Robustness Guarantees for Deep Neural Networks on Videos M Wu, M Kwiatkowska
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
34 2020 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
26 2019 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
19 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
12 * 2022 Verix: Towards verified explainability of deep neural networks M Wu, H Wu, C Barrett
Advances in Neural Information Processing Systems 36, 22247-22268, 2023
7 2023 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
5 2023 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
4 2024 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
1 2023 Robustness Evaluation of Deep Neural Networks with Provable Guarantees M Wu
University of Oxford, 2020
1 2020 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
2024 Soy: An Efficient MILP Solver for Piecewise-Affine Systems H Wu, M Wu, D Sadigh, C Barrett
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023
2023 Full Poincaré polarimetry enabled through physical inference: supplemental document C HE, J LIN, J CHANG, J ANTONELLO, BEN DAI, J WANG, J CUI, JI QI, ...
2022