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Min Wu
Min Wu
Department of Computer Science, Stanford University
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Safety verification of deep neural networks
X Huang, M Kwiatkowska, S Wang, M Wu
Computer Aided Verification: 29th International Conference, CAV 2017 …, 2017
11572017
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
5572020
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
3642018
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
1382020
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
1092019
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
492018
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
372018
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
342019
Robustness Guarantees for Deep Neural Networks on Videos
M Wu, M Kwiatkowska
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
332020
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
232018
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
212020
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
192024
Verix: Towards verified explainability of deep neural networks
M Wu, H Wu, C Barrett
Advances in Neural Information Processing Systems 36, 22247-22268, 2023
152023
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
14*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
92024
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
82023
Robustness Evaluation of Deep Neural Networks with Provable Guarantees
M Wu
University of Oxford, 2020
22020
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
12023
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
2024
Parallel Verification for -Equivalence of Neural Network Quantization
P Huang, Y Yang, H Wu, I Daukantas, M Wu, F Jia, C Barrett
International Symposium on AI Verification, 78-99, 2024
2024
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