Baylime: Bayesian local interpretable model-agnostic explanations X Zhao, W Huang, X Huang, V Robu, D Flynn Uncertainty in artificial intelligence, 887-896, 2021 | 79 | 2021 |
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 | 54* | 2021 |
Enhancing Adversarial Training with Second-Order Statistics of Weights G Jin, X Yi, W Huang, S Schewe, X Huang IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 49 | 2022 |
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, ... arXiv preprint arXiv:2305.11391, 2023 | 38 | 2023 |
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles X Zhao, W Huang, A Banks, V Cox, D Flynn, S Schewe, X Huang AISafety'21: Workshop on AI Safety at IJCAI-21, 2021 | 30* | 2021 |
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance Y Dong, W Huang, V Bharti, V Cox, A Banks, S Wang, X Zhao, S Schewe, ... ACM Transactions on Embedded Computing Systems, 2021 | 18* | 2021 |
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability W Huang, X Zhao, G Jin, X Huang Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023 | 14 | 2023 |
Embedding and extraction of knowledge in tree ensemble classifiers W Huang, X Zhao, X Huang Machine Learning 111 (5), 1925-1958, 2022 | 11 | 2022 |
Detecting operational adversarial examples for reliable deep learning X Zhao, W Huang, S Schewe, Y Dong, X Huang 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems …, 2021 | 11 | 2021 |
A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems Y Qi, PR Conmy, W Huang, X Zhao, X Huang AISafety'22: Workshop on AI Safety at IJCAI-22, 2022 | 8 | 2022 |
Hierarchical Distribution-Aware Testing of Deep Learning W Huang, X Zhao, A Banks, V Cox, X Huang ACM Transactions on Software Engineering and Methodology, 2023 | 7 | 2023 |
Tutorials on testing neural networks N Berthier, Y Sun, W Huang, Y Zhang, W Ruan, X Huang arXiv preprint arXiv:2108.01734, 2021 | 7 | 2021 |
Practical verification of neural network enabled state estimation system for robotics W Huang, Y Zhou, Y Sun, J Sharp, S Maskell, X Huang 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 7 | 2020 |
Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems W Huang, Y Zhou, G Jin, Y Sun, J Meng, F Zhang, X Huang Neurocomputing, 2024 | 6* | 2024 |
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems S Bensalem, CH Cheng, W Huang, X Huang, C Wu, X Zhao ISoLA'23: Int. Symp. on Leveraging Applications of Formal Methods …, 2023 | 5 | 2023 |
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System X Yuan, S Han, W Huang, H Ye, X Kong, F Zhang Computers & Security, 2023 | 1 | 2023 |
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models K Zhao, X Chen, Y Yu, X Kong, F Zhang, W Huang 2024 International Joint Conference on Neural Networks (IJCNN), 2024 | | 2024 |
Diversity supporting robustness: Enhancing adversarial robustness via differentiated ensemble predictions X Chen, W Huang, Z Peng, W Guo, F Zhang Computers & Security, 103861, 2024 | | 2024 |
Verification and Validation of Machine Learning Safety in Learning-Enabled Autonomous Systems W Huang PQDT-Global, 2023 | | 2023 |