2024 [1] Jing Zhao. Zengyu Qiu, Huiqin Hu, Shiliang Sun. HWLane: HW-Transformer for Lane Detection. IEEE Transactions on Intelligent Transportation Systems (2022) （中科院一区）（CCF A类）(一作) 2023 [2] Ding, Chaoyue, Shiliang Sun, and Jing Zhao *. MST-GAT: A multimodal spatial–temporal graph attention network for time series anomaly detection. Information Fusion 89 (2023): 527-536. （中科院一区）（通讯） [3] Shaofan Liu, Shiliang Sun, Jing Zhao *: Fair Transfer Learning with Factor Variational Auto-Encoder. Neural Process. Lett. 55(3): 2049-2061 (2023)（通讯） [4] Shiliang Sun, Jing Zhao *, Minghao Gu, Shanhu Wang: Variational Hybrid Monte Carlo for Efficient Multi-Modal Data Sampling. Entropy 25(4): 560 (2023)（通讯） [5] Chaoyue Ding, Jing Zhao *, Shiliang Sun. Concept Drift Adaptation for Time Series Anomaly Detection via Transformer. Neural Processing Letters (2022): 1-21. （通讯） [6] Wenbo Dong, Shiliang Sun *, Jing Zhao, Nan Zhang: Knowledge graph relation reasoning with variational reinforcement network. Inf. Fusion 100: 101900 (2023)（中科院一区） [7] Zhenbo Huang, Shiliang Sun *, Jing Zhao, Liang Mao: Multi-modal policy fusion for end-to-end autonomous driving. Inf. Fusion 98: 101834 (2023)（中科院一区） [8] Shiliang Sun *; Jingjing Fei; Jing Zhao; Liang Mao; Multi-view Collaborative Gaussian Process DynamicalSystems, Journal of Machine Learning Research, 2023, 24：1-32. （CCF A类） 2022 [9] Qiu, Zengyu, Jing Zhao *, and Shiliang Sun *. MFIALane: Multiscale Feature Information Aggregator Network for Lane Detection. IEEE Transactions on Intelligent Transportation Systems (2022). （中科院一区）（共同通讯）（CCF A类） [10] Jing Zhao, Zengyu Qiu, Shiliang Sun *: Multi-view multi-label active learning with conditional Bernoulli mixtures. Int. J. Mach. Learn. Cybern. 13(6): 1589-1601 (2022) （一作） [11] Shiliang Sun, Jing Zhao*, Ziang Dong. Conditional random fields for multiview sequential data modeling.IEEE Transactions on Neural Networks and Learning Systems SCI, (2022)(中科院一区) （通讯）（CCF A类） [12] Yujia Li, Shiliang Sun, Jing Zhao*: TiRGN: Time-Guided Recurrent Graph Network with Local-Global Historical Patterns for Temporal Knowledge Graph Reasoning. IJCAI 2022: 2152-2158（通讯）（CCF A类） [13] Tao Hu, Shiliang Sun, Jing Zhao, Dongyu Shi: Enhancing Unsupervised Domain Adaptation via Semantic Similarity Constraint for Medical Image Segmentation. IJCAI 2022: 3071-3077 [14] Ping Huang, Jing Zhao, Shiliang Sun *, Yichu Lin. (2022). Knowledge enhanced zero-resource machine translation using image-pivoting. Applied Intelligence, 1-13. [15] Chaoyue Ding, Shiliang Sun*, Jing Zhao: Multi-Modal Adversarial Example Detection with Transformer. IJCNN 2022: 1-7 [16] Tao Zhang, Shiliang Sun*, Jing Zhao: Robust Cross-Modal Retrieval by Adversarial Training. IJCNN 2022: 1-8 [17] Yi Zhang, Jing Zhao*, Shiliang Sun. Diverse Machine Translation with Translation Memory. IJCNN 2022: 1-8. （通讯） [18] Shiqi Guo, Yumeng Si, Jing Zhao*：Abstractive Summarization Model with Adaptive Sparsemax. NLPCC (1) 2022: 810-821（通讯） [19] Zengyu Qiu, Jing Zhao*, Shiliang Sun. Enhancing Robustness of Lane Detection through Dynamic Smoothness. ICAUS, 2022（通讯） 2021 [20] Lidan Wu, Daoming Zong, Shiliang Sun, Jing Zhao*: A Sequential Contrastive Learning Framework for Robust Dysarthric Speech Recognition. ICASSP 2021: 7303-7307 （CCF B类）（通讯） [21] Chaoyue Ding, Shiliang Sun, Jing Zhao*: Multi-Task Transformer with Input Feature Reconstruction for Dysarthric Speech Recognition. ICASSP 2021: 7318-7322 （CCF B类）（通讯） [22] Zehui Cao#, Jing Zhao#, Shiliang Sun*: Stick-Breaking Dependent Beta Processes with Variational Inference. Neural Process. Lett. 53(1): 339-353 (2021)(CCF C类期刊) （共同一作） [23] Jing Zhao, Yi Zhang, Shiliang Sun*, Haiwei Dai: Variational Beta Process Hidden Markov Models with Shared Hidden States for Trajectory Recognition. Entropy 23(10): 1290 (2021) （中科院三区）（一作） [24] Shiqi Guo, Jing Zhao*, Shiliang Sun: Resilient Abstractive Summarization Model with Adaptively Weighted Training Loss. IJCNN 2021: 1-8 （CCF C类）（通讯） [25] Xiao Liu, Jing Zhao, Shiliang Sun*, Huawen Liu, Hao Yang: Variational multimodal machine translation with underlying semantic alignment. Inf. Fusion 69: 73-80 (2021)（中科院一区） [26] Daoming Zong, Shiliang Sun*, Jing Zhao: ASHF-Net: Adaptive Sampling and Hierarchical Folding Network for Robust Point Cloud Completion. AAAI 2021: 3625-3632（CCF A类） 2020 [27] Jing Zhao, Shiliang Sun*, Huijuan Wang, Zehui Cao: Promoting active learning with mixtures of Gaussian processes. Knowl. Based Syst. 188 (2020) (中科院一区)（一作）（CCF A类） [28] Shiliang Sun, Zehui Cao, Han Zhu, Jing Zhao*: A Survey of Optimization Methods From a Machine Learning Perspective. IEEE Trans. Cybern. 50(8): 3668-3681 (2020) (中科院一区)（通讯）（CCF A类） [29] Yueyue Hu, Shiliang Sun, Xin Xu, Jing Zhao*: Multi-View Deep Attention Network for Reinforcement Learning (Student Abstract). AAAI 2020: 13811-13812（CCF A类）（通讯）（CCF A类） [30] Xiao Liu, Jing Zhao*, Shiliang Sun: Bayesian Adversarial Attack on Graph Neural Networks (Student Abstract). AAAI 2020: 13867-13868 （CCF A类） （通讯）（CCF A类） [31] Jing Zhao, Xiao Liu, Shaojie He, Shiliang Sun*: Probabilistic inference of Bayesian neural networks with generalized expectation propagation. Neurocomputing 412: 392-398 (2020) (中科院二区)（一作） [32] Yueyue Hu, Shiliang Sun*, Xin Xu, Jing Zhao: Attentive multi-view reinforcement learning. Int. J. Mach. Learn. Cybern. 11(11): 2461-2474 (2020) (中科院二区) [33] Han Zhu, Jing Zhao*, Shiliang Sun: Multi-view Deep Gaussian Process with a Pre-training Acceleration Technique. PAKDD (2) 2020: 299-311（CCF C类）（通讯） 2019 [34] Ziang Dong, Jing Zhao*, Shiliang Sun: A Conditional Random Fields Based Framework for Multiview Sequential Data Modeling. ICONIP (5) 2019: 698-706（CCF C类）（通讯） 2018 [35]J. Wang, J. Zhao*, S. Sun and D. Shi. Intelligent educational data analysis with Gaussian processes. Proceedings of the 25th International Conference on Neural Information Processing (ICONIP), pp. 353-362, 2018. (CCF C类会议)（通讯） [36]J. Fei, J. Zhao#, S. Sun* and Yan Liu. Active learning methods with deep Gaussian processes. Proceedings of the 25th International Conference on Neural Information Processing (ICONIP), pp. 473-483, 2018. (CCF C类会议)（共同一作） [37]J. Chen#, S. Sun#, J. Zhao*. Multi-label active learning with conditional Bernoulli mixtures. Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI), pp. 954-967, 2018. (CCF C类会议)（通讯） 2017 [38]J. Zhao, X. Xie, X. Xu, S. Sun*. Multi-view learning overview: Recent progress and new challenges. Information Fusion (IF), 38: 43-54, 2017. (SCI一区期刊, IF：6.639，ESI高被引论文，Top 1%)（一作）（CCF A类） [39]H. Wang# and J. Zhao# and Z. Tang and S. Sun*. Educational and non-educational text classification based on deep Gaussian processes. Proceedings of the 24th International Conference on Neural Information Processing (ICONIP), pp. 415-423, 2017. (CCF C类会议)（共同一作） [40]C. Luo, S. Sun, J. Zhao*. Variational hidden conditional random fields with beta processes. Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 1887-1893, 2017. (EI会议)（通讯） 2016 [41]J. Zhao and S. Sun#*. Variational dependent multi-output Gaussian process dynamical systems. Journal of Machine Learning Research (JMLR), 17: 1-36, 2016. (SCI期刊, CCF A类期刊，IF:5)（共同一作）（CCF A类） [42]J. Zhao and S. Sun*. High-order Gaussian process dynamical models for traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems (TITS), 17: 2014-2019, 2016. (SCI一区期刊, IF:3.724)（中科院一区）（一作）（CCF A类） [43]M. Yin#, J. Zhao#, S. Sun*. Key course selection for academic early warning based on Gaussian processes. The 17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), pp. 240-247, 2016. (EI会议)（共同一作） 2015 [44] J. Zhao and S. Sun*. Revisiting Gaussian process dynamical models. In Proceedings of the 24th International Joint Conference Artificial Intelligence (IJCAI), pp. 1047-1053, 2015. (CCF A类会议)（一作）（CCF A类） [45]S. Sun* and J. Zhao and J. Zhu. A review of Nyström methods for large-scale machine learning. Information Fusion (IF), 26:36-48, 2015. (SCI一区期刊，IF：4.353) [46]S. Sun* and J. Zhao and Q. Gao. Modeling and recognizing human trajectories with beta process hidden Markov models. Pattern Recognition (PR), 48: 2407-2417, 2015. (SCI一区期刊，IF:3.399) 2014 [47]J. Zhao and S. Sun*. Variational dependent multi-output Gaussian process dynamical systems. In Proceedings of the 17th International Conference of Discovery Science (DS), 8777: 350-361, 2014. (EI会议)（一作） 授权发明专利： [1] 孙仕亮；戴海威；赵静..一种基于变分BP-HMM的人的行为轨迹识别方法 [2] 孙仕亮；刘啸；赵静；张楠. 基于变分推理和多任务学习的多模态机器翻译方法 [3] 赵静；邱增玉；孙仕亮. 一种基于主动学习多视图多标签分类器的构建方法 [4] 张楠；孙仕亮；赵静.基于缺失图重构和自适应近邻的不完整多视图聚类方法 [5] 孙仕亮；刘禹含；赵静. 一种蛋白质知识图谱向量化方法 [6] 赵静；林奕初；张艺；孙仕亮. 一种运用辅助记忆的多样性机器翻译方法 [7] 孙仕亮；丁超越；赵静. 基于对抗训练的半监督CT图像分割方法 [8] 孙仕亮；赵静；张涛；张庆久. 一种基于提示学习的跨模态检索对抗防御方法 [9] 赵静；孙仕亮；李宇佳. 一种基于关系自适应网络的小样本时态知识图谱补全方法 [10]孙仕亮；余梦然；赵静；毛亮. 一种面向扰动奖励的深度强化学习对抗防御方法 [11]赵静；胡惠琴；孙仕亮；王振超. 一种基于分割点和双特征增强的车道线检测方法及系统 [12]孙翊铭；赵静. 一种基于时序自适应卷积与注意力机制的目标跟踪方法 | 