（* 代表 equal contribution）
 

         可解释、可信人工智能方向：

1. Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, Quanshi Zhang. Unifying Fourteen Post-Hoc Attribution Methods With Taylor Interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 (IEEE TPAMI；CCF-A类期刊；IF = 20.8，详见知乎介绍：论统一14种输入重要性归因算法)

2. Huiqi Deng^*, Qihan Ren^*, Hao Zhang, Quanshi Zhang. Discovering and Explaining the Representation Bottleneck of DNNs. International Conference on Learning Representations, 2022 (ICLR Oral Paper 录用率1.6%；清华 A 类会议；详见知乎介绍：神经网络的知识表达瓶颈)

3. Dongrui Liu^*, Huiqi Deng^*, Xu Cheng, Qihan Ren, Kangrui Wang, Quanshi Zhang. Towards the difficulty for a deep neural network to learn concepts of different complexities. Conference and Workshop onNeural Information Processing Systems, 2023 (NeurIPS；CCF-A 类会议）

4. Qihan Ren^*, Huiqi Deng^*, Yunuo Chen, Siyu Lou, Quanshi Zhang. Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts. International Conference on Machine Learning, 2023（ ICML; CCF-A类会议）

5. Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu. A Unified Taylor Framework for Revisiting Attribution Methods. AAAI Conference on Artificial Intelligence, 2021（AAAI; CCF-A类会议）

6. Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu. Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution. ACM SIGKDD Conference on Knowledge Discovery and Data Mining （KDD; CCF-A类会议）

7. Huiqi Deng, Weifu Chen, Qi Shen, Andy J. Ma, Pong C. Yuen, Guocan Feng. Invariant subspace learning for time series data based on dynamic time warping distance. Pattern Recognition, 2020 (PR, CCF-B类期刊，中科院一区)

8. Huiqi Deng, Weifu Chen, Andy J. Ma, Qi Shen, Pong C. Yuen, Guocan Feng. Robust shapelets learning: Transform-invariant prototypes. Chinese Conference on Pattern Recognition and Computer Vision, 2018 (PRCV, CCF-C类会议)

9. Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du. Explainability for large language models: A survey. ACM Transactions on Intelligent Systems and Technology, 2024. (大模型可解释性综述，引用超 500 次)

10. Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, Quanshi Zhang. Explaining generalization power of a dnn using interactive concepts. AAAI Conference on Artificial Intelligence, 2024（AAAI; CCF-A类会议）

11. Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang. Defining and quantifying the emergence of sparse concepts in dnns. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. （CVPR; CCF-A类会议）
    
    
    AI 应用方向：

12. Hongbin Pei, Yu Li, Huiqi Deng, Jingxin Hai, Pinghui Wang, Jie Ma, Jing Tao, Yuheng Xiong, Xiaohong Guan. Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing. International Conference on Machine Learning, 2024（ ICML; CCF-A类会议；Spotlight Paper 录用率3.5%；算法应用在KDD CUP 2024 Task 2挑战赛，获得铜牌）  

13. Hongbin Pei, Taile Chen, Chen A, Huiqi Deng, Jing Tao, Pinghui Wang, Xiaohong Guan. HAGO-Net: Hierarchical Geometric Massage Passing for Molecular Representation Learning. AAAI Conference on Artificial Intelligence, 2024（AAAI; CCF-A类会议）

14. Qingxiong Tan, Andy J. Ma, Mang Ye, Baoyao Yang, Huiqi Deng, Vincent Wai-Sun Wong, et al. UA-CRNN: Uncertainty-aware convolutional recurrent neural network for mortality risk prediction.   ACM international conference on information and knowledge management, 2019 (CIKM, CCF-B类会议)