- 梁建青
- 最终学位:博士
- 电子邮箱:liangjq@sxu.edu.cn
- 导师类型:博士生导师
- 联系电话:0351-7010566
- 所在院所:2138CC太阳集团
- 研究方向:机器学习、模式识别、大数据分析技术、人工智能
- 个人简介
- 学术论文
- 科研项目
梁建青,博士,副教授,博士生导师。主要研究方向为机器学习、模式识别、大数据分析技术、人工智能。近年来,在AI、TPAMI、TIP、TCYB、TMM、PR、《软件学报》、ICML、AAAI、IJCAI、WSDM等国际国内重要学术刊物上发表学术论文30余篇。获2023年山西省自然科学一等奖 (4/4),2024年山西省科技创新领域青年拔尖人才,2024年2138cn太阳集团古天乐文瀛青年学者,2019年天津大学优秀博士学位论文奖,WSDM2022杰出审稿人奖以及CCML2017最佳学生论文奖。主持、参与国家自然科学基金青年项目、面上项目、科技部重大项目、国家自然科学基金重点联合基金项目等10余项。申请发明专利14项,获得软件著作权登记9项。担任人工智能领域国际学术期刊和会议 IJCV、TMM、TNNLS、PR、ICML、NeurIPS、ICCV、ICLR、KDD、AAAI、IJCAI、WSDM 审稿人,ICME 和 PRCV 领域主席。
[1] J Liang*, Z Li, X Wei, Y Liu, Z Wang*. ML2-GCL: Manifold Learning Inspired Lightweight Graph Contrastive Learning. In Proceedings of the 42nd International Conference on Machine Learning, 2025
[2] Z Wang, X Wang, J Liang*. CSG-ODE: Control Synth Graph ODE For Modeling Complex Evolution of Dynamic Graphs. In Proceedings of the 42nd International Conference on Machine Learning, 2025
[3] Z Wang, J Wen, J Liang*. Delay-DSGN: A Dynamic Spiking Graph Neural Network with Delay Mechanisms for Evolving Graph. In Proceedings of the 42nd International Conference on Machine Learning, 2025
[4] J Cui, Q Yue, J Liang*, J Liang*. Human Cognition-Inspired Hierarchical Fuzzy Learning Machine. In Proceedings of the 42nd International Conference on Machine Learning, 2025
[5] C Fang, J Liang*, J Liang, H Wang, K Yao, F Cao. Multi-Modal Point Cloud Completion with Interleaved Attention Enhanced Transformer. In Proceedings of the 34th International Joint Conference on Artificial Intelligence, 2025
[6] Y Fan, J Cui, J Liang, J Liang*. Open-World Semi-Supervised Learning with Class Semantic Correlations. In Proceedings of the 34th International Joint Conference on Artificial Intelligence, 2025
[7] Z Li, J Wang, J Liang*, J Cui, X Zhao, J Liang. Uncertainty-guided Graph Contrastive Learning from a Unified Perspective. In Proceedings of the 34th International Joint Conference on Artificial Intelligence, 2025
[8] J Liang, X Wei, M Chen, Z Wang, J Liang*. Gnn-transformer cooperative architecture for trustworthy graph contrastive learning. In Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence, 2025
[9] Q Yue, J Cui, J Liang*, L Bai*. Class semantic attribute perception guided zero-shot learning. In Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence, 2025
[10] Z Wang, J Pan, X Zhao*, J Liang, C Feng, K Yao. Counterfactual task-augmented meta-learning for cold-start sequence recommendation. In Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence, 2025
[11] Z Wang, J Guo, J Liang*, J Liang, S Cheng, J Zhang. Graph segmentation and contrastive enhanced explainer for graph neural networks. In Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence, 2025
[12] J Liang, M Chen, J Liang*. Graph external attention enhanced transformer. In Proceedings of the 41st International Conference on Machine Learning, 2024, 1191: 29560-29574
[13] Z Du, J Liang, J Liang*, K Yao, F Cao. Graph regulation network for point cloud segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, DOI: 10.1109/TPAMI.2024.3400402
[14] Q Yue, J Cui, L Bai*, J Liang, J Liang. A zero-shot learning boosting framework via concept-constrained clustering. Pattern Recognition, 2023, 109937
[15] J Liang*, Z Du, J Liang, K Yao, F Cao. Long and short-range dependency graph structure learning framework on point cloud. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, DOI: 10.1109/TPAMI.2023.3298711
[16] J Cui, J Liang, Q Yue, J Liang*. A general representation learning framework with generalization performance guarantees. In Proceedings of the 40th International Conference on Machine Learning, 2023
[17] S Tang, K Yao*, J Liang, Z Wang, J Liang. Graph neural networks with interlayer feature representation for image super-resolution. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023: 652-660
[18] X Guo, W Wei*, J Liang, C Dang, J Liang. Metric learning via perturbing hard-to-classify instances. Pattern Recognition, 2022, 132: 108928
[19] K Yao, J Liang*, J Liang, M Li, F Cao. Multi-view graph convolutional networks with attention mechanism. Artificial Intelligence, 2022, 307: 103708
[20] J Wang, J Liang*, J Liang, K Yao. GUIDE: Training deep graph neural networks via guided dropout over edges. IEEE Transactions on Neural Networks and Learning Systems, 2022, DOI: 10.1109/TNNLS.2022.3172879
[21] T Guo, J Liang*, J Liang, GS Xie. Cross-modal propagation network for generalized zero-shot learning. Pattern Recognition Letters, 2022, 159: 125-131
[22] J Liang, P Zhu*, C Dang, Q Hu*. Semisupervised laplace-regularized multimodality metric learning. IEEE Transactions on Cybernetics, 2022, 52(5): 2955-2967
[23] J Wang, J Liang*, K Yao, J Liang, D Wang. Graph convolutional autoencoders with co-learning of graph structure and node attributes. Pattern Recognition, 2022, 121: 108215
[24] J Liang, Q Hu*, C Dang, W Zuo. Weighted graph embedding-based metric learning for kinship verification. IEEE Transactions on Image Processing, 2019, 28(3): 1149-1162
[25] J Liang, Q Hu*, P Zhu, W Wang. Efficient multi-modal geometric mean metric learning. Pattern Recognition, 2018, 75: 188-198
[26] J Liang, Q Hu*, W Wang, Y Han. Semisupervised online multikernel similarity learning for image retrieval. IEEE Transactions on Multimedia, 2017, 19(5): 1077-1089
[27] 齐忍, 朱鹏飞*, 梁建青. 混杂数据的多核几何平均度量学习. 软件学报, 2017, 28(11): 2992-3001
[28] J Liang, Y Han, Q Hu*. Semi-supervised image clustering with multi-modal information. Multimedia Systems, 2016, 22(2): 149-160
[29] C Dang*, J Liang, Y Yang. A deterministic annealing algorithm for approximating a solution of the linearly constrained nonconvex quadratic minimization problem. Neural Networks, 2013, 39: 1-11
1. 监督信息受限的多视图图表示学习方法研究, 国家自然科学基金面上项目, 2024.01-2027.12, 主持
2. 面向高维数据的多视图距离度量学习, 国家自然科学基金青年项目, 2021.01-2023.12, 主持
3. 认知计算基础理论与方法研究, 科技创新2030-“新一代人工智能”科技部重大项目, 2021.1-2024.10, 参与
4. 网络大数据分析挖掘的理论与方法, 国家自然科学基金联合基金项目, 2022.01-2025.12, 参与
5. 复杂多视图数据统一表示及分类研究, 国家自然科学基金面上项目, 2020.1-2023.12, 参与
6. 半配对的图像和文本异构迁移学习方法研究, 国家自然科学基金青年项目, 2018.1-2020.12, 参与
7. 面向复杂多视角数据的层次聚类研究, 国家自然科学基金青年项目, 2017.1-2019.12, 参与
8. 大规模异构数据匹配的距离度量学习, 国家自然科学基金青年项目, 2016.1-2018.12, 参与
9. 粗糙回归模型与算法研究, 国家自然科学基金青年项目, 2016.1-2018.12, 参与