| Interpreting and improving adversarial robustness of deep neural networks with neuron sensitivity C Zhang, A Liu, X Liu, Y Xu, H Yu, Y Ma, T Li IEEE Transactions on Image Processing 30, 1291-1304, 2020 | 103 | 2020 |
| NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks X Xie, T Li, J Wang, L Ma, Q Guo, F Juefei-Xu, Y Liu ACM Transactions on Software Engineering and Methodology (TOSEM) 31 (3), 1-27, 2022 | 39 | 2022 |
| Knowledge consistency between neural networks and beyond R Liang, T Li, L Li, J Wang, Q Zhang ICLR 2020, 2019 | 38 | 2019 |
| Understanding adversarial robustness via critical attacking route T Li, A Liu, X Liu, Y Xu, C Zhang, X Xie Information Sciences 547, 568-578, 2021 | 23 | 2021 |
| Personalization as a shortcut for few-shot backdoor attack against text-to-image diffusion models Y Huang, F Juefei-Xu, Q Guo, J Zhang, Y Wu, M Hu, T Li, G Pu, Y Liu Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21169 …, 2024 | 16* | 2024 |
| On the robustness of segment anything Y Huang, Y Cao, T Li, F Juefei-Xu, D Lin, IW Tsang, Y Liu, Q Guo arXiv preprint arXiv:2305.16220, 2023 | 13 | 2023 |
| Badedit: Backdooring large language models by model editing Y Li, T Li, K Chen, J Zhang, S Liu, W Wang, T Zhang, Y Liu arXiv preprint arXiv:2403.13355, 2024 | 10 | 2024 |
| Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing Y Xiao, A Liu, T Li, X Liu ISSTA 2023, 2023 | 10 | 2023 |
| FAIRER: fairness as decision rationale alignment T Li, Q Guo, A Liu, M Du, Z Li, Y Liu International Conference on Machine Learning, 19471-19489, 2023 | 9 | 2023 |
| A mutation-based method for multi-modal jailbreaking attack detection X Zhang, C Zhang, T Li, Y Huang, X Jia, X Xie, Y Liu, C Shen arXiv preprint arXiv:2312.10766, 2023 | 8 | 2023 |
| Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation C Wang, J Zhang, Y Feng, T Li, W Sun, Y Liu, X Peng arXiv preprint arXiv:2401.06391, 2024 | 5 | 2024 |
| Fairness via Group Contribution Matching. T Li, Z Li, A Li, M Du, A Liu, Q Guo, G Meng, Y Liu IJCAI, 436-445, 2023 | 5* | 2023 |
| Faire: Repairing fairness of neural networks via neuron condition synthesis T Li, X Xie, J Wang, Q Guo, A Liu, L Ma, Y Liu ACM Transactions on Software Engineering and Methodology 33 (1), 1-24, 2023 | 3 | 2023 |
| Learning to Locate and Describe Vulnerabilities J Zhang, S Liu, X Wang, T Li, Y Liu 2023 38th IEEE/ACM International Conference on Automated Software …, 2023 | 3 | 2023 |
| FedMut: Generalized Federated Learning via Stochastic Mutation M Hu, Y Cao, A Li, Z Li, C Liu, T Li, M Chen, Y Liu Proceedings of the AAAI Conference on Artificial Intelligence 38 (11), 12528 …, 2024 | 2 | 2024 |
| Purifying large language models by ensembling a small language model T Li, Q Liu, T Pang, C Du, Q Guo, Y Liu, M Lin arXiv preprint arXiv:2402.14845, 2024 | 2 | 2024 |
| Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One T Li, X Zhang, C Du, T Pang, Q Liu, Q Guo, C Shen, Y Liu arXiv preprint arXiv:2402.12150, 2024 | 2 | 2024 |
| Improving robustness of lidar-camera fusion model against weather corruption from fusion strategy perspective Y Huang, K Yu, Q Guo, F Juefei-Xu, X Jia, T Li, G Pu, Y Liu arXiv preprint arXiv:2402.02738, 2024 | 2 | 2024 |
| Unveiling project-specific bias in neural code models Z Li, Y Li, T Li, M Du, B Wu, Y Cao, J Jiang, Y Liu arXiv preprint arXiv:2201.07381, 2022 | 2 | 2022 |
| MeTMaP: Metamorphic Testing for Detecting False Vector Matching Problems in LLM Augmented Generation G Wang, Y Li, Y Liu, G Deng, T Li, G Xu, Y Liu, H Wang, K Wang Proceedings of the 2024 IEEE/ACM First International Conference on AI …, 2024 | 1 | 2024 |
Yang LiuNanyang Technological University在 ntu.edu.sg 的电子邮件经过验证
