| Safety verification of deep neural networks X Huang, M Kwiatkowska, S Wang, M Wu International Conference on Computer Aided Verification, 3-29, 2017 | 1183 | 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 | 570 | 2020 |
| Concolic Testing for Deep Neural Networks Y Sun, M Wu, W Ruan, X Huang, M Kwiatkowska, D Kroening ASE2018, 2018 | 362 | 2018 |
| Reachability Analysis of Deep Neural Networks with Provable Guarantees W Ruan, X Huang, M Kwiatkowska IJCAI2018, 2018 | 339 | 2018 |
| Testing deep neural networks Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore arXiv preprint arXiv:1803.04792, 2018 | 306 | 2018 |
| Feature-guided black-box safety testing of deep neural networks M Wicker, X Huang, M Kwiatkowska Tools and Algorithms for the Construction and Analysis of Systems: 24th …, 2018 | 290 | 2018 |
| Structural test coverage criteria for deep neural networks Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore Proceedings of the 41st International Conference on Software Engineering …, 2019 | 143 | 2019 |
| 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 |
| Baylime: Bayesian local interpretable model-agnostic explanations X Zhao, W Huang, X Huang, V Robu, D Flynn Uncertainty in Artificial Intelligence, 887-896, 2021 | 120 | 2021 |
| Spatial Uncertainty-Aware Semi-Supervised Crowd Counting Y Meng, H Zhang, Y Zhao, X Yang, X Qian, X Huang, Y Zheng Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 118 | 2021 |
| 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 |
| A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation X Huang, W Ruan, W Huang, G Jin, Y Dong, C Wu, S Bensalem, R Mu, ... Artificial Intelligence Review, 2024 | 103 | 2024 |
| Analyzing deep neural networks with symbolic propagation: Towards higher precision and faster verification J Li, J Liu, P Yang, L Chen, X Huang, L Zhang Static Analysis: 26th International Symposium, SAS 2019, Porto, Portugal …, 2019 | 101 | 2019 |
| Graph-based region and boundary aggregation for biomedical image segmentation Y Meng, H Zhang, Y Zhao, X Yang, Y Qiao, IJC MacCormick, X Huang, ... IEEE transactions on medical imaging 41 (3), 690-701, 2021 | 78 | 2021 |
| DeepConcolic: testing and debugging deep neural networks Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore 2019 IEEE/ACM 41st International Conference on Software Engineering …, 2019 | 78 | 2019 |
| Enhancing Adversarial Training with Second-Order Statistics of Weights G Jin, X Yi, W Huang, S Schewe, X Huang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 69 | 2022 |
| An epistemic strategy logic X Huang, RVD Meyden ACM Transactions on Computational Logic (TOCL) 19 (4), 26, 2018 | 68* | 2018 |
| CNN-GCN aggregation enabled boundary regression for biomedical image segmentation Y Meng, M Wei, D Gao, Y Zhao, X Yang, X Huang, Y Zheng Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 63 | 2020 |
| Symbolic model checking epistemic strategy logic X Huang, R van der Meyden Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence …, 2014 | 61 | 2014 |
| Coverage-guided testing for recurrent neural networks W Huang, Y Sun, X Zhao, J Sharp, W Ruan, J Meng, X Huang IEEE Transactions on Reliability 71 (3), 1191-1206, 2021 | 60 | 2021 |
Wenjie RuanSenior Lecturer, University of ExeterVerified email at cs.ox.ac.uk
