| Towards characterizing adversarial defects of deep learning software from the lens of uncertainty X Zhang, X Xie, L Ma, X Du, Q Hu, Y Liu, J Zhao, M Sun Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 80 | 2020 |
| Decision-guided weighted automata extraction from recurrent neural networks X Zhang, X Du, X Xie, L Ma, Y Liu, M Sun Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 11699 …, 2021 | 18 | 2021 |
| Using Coq for formal modeling and verification of timed connectors W Hong, MS Nawaz, X Zhang, Y Li, M Sun Software Engineering and Formal Methods: SEFM 2017 Collocated Workshops …, 2018 | 13 | 2018 |
| Reasoning about connectors in Coq X Zhang, W Hong, Y Li, M Sun International Workshop on Formal Aspects of Component Software, 172-190, 2016 | 13 | 2016 |
| Reasoning about connectors using Coq and Z3 X Zhang, W Hong, Y Li, M Sun Science of Computer Programming 170, 27-44, 2019 | 8 | 2019 |
| Capturing stochastic and real-time behavior in reo connectors Y Li, X Zhang, Y Ji, M Sun Brazilian Symposium on Formal Methods, 287-304, 2017 | 8 | 2017 |
| Uncertainty-guided testing and robustness enhancement for deep learning systems X Zhang Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 7 | 2020 |
| Safe inputs approximation for black-box systems B Xue, Y Liu, L Ma, X Zhang, M Sun, X Xie 2019 24th International Conference on Engineering of Complex Computer …, 2019 | 7 | 2019 |
| Using Z3 for formal modeling and verification of FNN global robustness Y Zhang, Z Wei, X Zhang, M Sun arXiv preprint arXiv:2304.10558, 2023 | 6 | 2023 |
| Extracting weighted finite automata from recurrent neural networks for natural languages Z Wei, X Zhang, M Sun International Conference on Formal Engineering Methods, 370-385, 2022 | 6 | 2022 |
| DeepGlobal: A framework for global robustness verification of feedforward neural networks W Sun, Y Lu, X Zhang, M Sun Journal of Systems Architecture 128, 102582, 2022 | 6 | 2022 |
| Towards a formally verified EVM in production environment X Zhang, Y Li, M Sun Coordination Models and Languages: 22nd IFIP WG 6.1 International Conference …, 2020 | 6 | 2020 |
| A formal framework capturing real-time and stochastic behavior in connectors Y Li, X Zhang, Y Ji, M Sun Science of Computer Programming 177, 19-40, 2019 | 5 | 2019 |
| A relational model for probabilistic connectors based on timed data distribution streams M Sun, X Zhang Formal Modeling and Analysis of Timed Systems: 16th International Conference …, 2018 | 4 | 2018 |
| Weighted automata extraction and explanation of recurrent neural networks for natural language tasks Z Wei, X Zhang, Y Zhang, M Sun Journal of Logical and Algebraic Methods in Programming 136, 100907, 2024 | 3 | 2024 |
| When to Trust AI: Advances and Challenges for Certification of Neural Networks M Kwiatkowska, X Zhang 2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS …, 2023 | 3 | 2023 |
| On preimage approximation for neural networks X Zhang, B Wang, M Kwiatkowska arXiv preprint arXiv:2305.03686, 2023 | 3 | 2023 |
| Using recurrent neural network to predict tactics for proving component connector properties in Coq X Zhang, Y Li, W Hong, M Sun 2019 International Symposium on Theoretical Aspects of Software Engineering …, 2019 | 3 | 2019 |
| Towards Formal Modeling and Verification of Probabilistic Connectors in Coq (S). X Zhang, M Sun SEKE, 385-384, 2018 | 3 | 2018 |
| SMT-based modeling and verification of cloud applications X Zhang, M Sun Services–SERVICES 2019: 15th World Congress, Held as Part of the Services …, 2019 | 2 | 2019 |
Meng SunProfessor, School of Mathematical Science, Peking UniversityVerified email at math.pku.edu.cn
