Safety verification of deep neural networks X Huang, M Kwiatkowska, S Wang, M Wu International Conference on Computer Aided Verification, 3-29, 2017 | 1087 | 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 | 511 | 2020 |
Concolic Testing for Deep Neural Networks Y Sun, M Wu, W Ruan, X Huang, M Kwiatkowska, D Kroening ASE2018, 2018 | 339 | 2018 |
Reachability Analysis of Deep Neural Networks with Provable Guarantees W Ruan, X Huang, M Kwiatkowska IJCAI2018, 2018 | 313 | 2018 |
Testing deep neural networks Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore arXiv preprint arXiv:1803.04792, 2018 | 281 | 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 | 269 | 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 | 128 | 2020 |
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 | 115 | 2019 |
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 | 100 | 2019 |
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 | 91 | 2021 |
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 | 91 | 2019 |
Baylime: Bayesian local interpretable model-agnostic explanations X Zhao, W Huang, X Huang, V Robu, D Flynn Uncertainty in Artificial Intelligence, 887-896, 2021 | 86 | 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 | 65 | 2019 |
An epistemic strategy logic X Huang, RVD Meyden ACM Transactions on Computational Logic (TOCL) 19 (4), 26, 2018 | 65* | 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 | 58 | 2020 |
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 | 57 | 2021 |
Symbolic model checking epistemic strategy logic X Huang, R van der Meyden Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence …, 2014 | 55 | 2014 |
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 | 50 | 2022 |
The signaling pathway of Caenorhabditis elegans mediates chemotaxis response to the attractant 2-heptanone in a trojan horse-like pathogenesis C Zhang, N Zhao, Y Chen, D Zhang, J Yan, W Zou, K Zhang, X Huang Journal of Biological Chemistry 291 (45), 23618-23627, 2016 | 50 | 2016 |
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 | 48 | 2024 |