| A survey on security threats and defensive techniques of machine learning: A data driven view Q Liu, P Li, W Zhao, W Cai, S Yu, VCM Leung IEEE access 6, 12103-12117, 2018 | 521 | 2018 |
| A simple feature augmentation for domain generalization P Li, D Li, W Li, S Gong, Y Fu, TM Hospedales Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 226 | 2021 |
| Striking a balance between stability and plasticity for class-incremental learning G Wu, S Gong, P Li Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 69 | 2021 |
| Ranking distance calibration for cross-domain few-shot learning P Li, S Gong, C Wang, Y Fu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 47 | 2022 |
| Chronic poisoning against machine learning based IDSs using edge pattern detection P Li, Q Liu, W Zhao, D Wang, S Wang 2018 IEEE International Conference on Communications (ICC), 1-7, 2018 | 28* | 2018 |
| Poisoning machine learning based wireless IDSs via stealing learning model P Li, W Zhao, Q Liu, X Liu, L Yu International Conference on Wireless Algorithms, Systems, and Applications …, 2018 | 21 | 2018 |
| 机器学习安全性问题及其防御技术研究综述 李盼， 赵文涛， 刘强， 崔建京， 殷建平 计算机科学与探索 12 (2), 171-184, 2018 | 21* | 2018 |
| A non-parametric graph clustering framework for multi-view data S Yu, S Wang, Z Dong, W Tu, S Liu, Z Lv, P Li, M Wang, E Zhu Proceedings of the AAAI Conference on Artificial Intelligence 38 (15), 16558 …, 2024 | 18 | 2024 |
| Network embedding-based anomalous density searching for multi-group collaborative fraudsters detection in socialmedia C Zhu, W Zhao, Q Li, P Li, Q Da Computers, Materials and Continua, 2019 | 12 | 2019 |
| Defense against poisoning attack via evaluating training samples using multiple spectral clustering aggregation method W Zhao, P Li, C Zhu, D Liu, X Liu Computers, Materials and Continua, 2019 | 12 | 2019 |
| CM. 2018 L Qiang, L Pan, Z Wentao, C Wei, Y Shui, L Victor A survey on security threats and defensive techniques of machine learning: A …, 2018 | 10 | 2018 |
| Semi-supervised few-shot learning with pseudo label refinement P Li, G Wu, S Gong, X Lan 2021 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2021 | 7 | 2021 |
| Regularising Knowledge Transfer by Meta Functional Learning. P Li, Y Fu, S Gong IJCAI, 2687-2693, 2021 | 5 | 2021 |
| Selecting the optimal hidden layer of extreme learning machine using multiple kernel learning W Zhao, P Li, Q Liu, D Liu, X Liu KSII Transactions on Internet and Information Systems (TIIS) 12 (12), 5765-5781, 2018 | 4 | 2018 |
| Mantra: Mutation Testing of Hardware Design Code Based on Real Bugs J Wu, Y Lei, Z Zhang, X Meng, D Yang, P Li, J He, X Mao 2023 60th ACM/IEEE Design Automation Conference (DAC), 1-6, 2023 | 3 | 2023 |
| Model capacity vulnerability in hyper-parameters estimation W Zhao, X Liu, Q Liu, J Chen, P Li IEEE Access 8, 21602-21612, 2020 | 2 | 2020 |
| A Framework of Meta Functional Learning for Regularising Knowledge Transfer P Li, Y Fu, S Gong arXiv preprint arXiv:2203.14840, 2022 | | 2022 |
| Scalable Poisoning Against Regression-Type Edge Computing Applications via An Approximate Optimization Strategy X Liu, W Zhao, P Li, S Jiao, Q Liu IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops …, 2019 | | 2019 |
| Supplementary Material: Ranking Distance Calibration for Cross-Domain Few-Shot Learning P Li, S Gong, C Wang, Y Fu | | |