最终学位：博士
导师类型：博士生导师
电子邮箱：liangjq@sxu.edu.cn
联系电话：0351-7010566
研究方向：机器学习、模式识别
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、WSDM等国际国内重要学术刊物上发表学术论文二十余篇。获山西省自然科学一等奖，澳门新甫京娱乐娱城平台文瀛青年学者，天津大学2019年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。主持国家自然科学基金青年项目1项、面上项目1项，参与科技部重大项目1项、国家自然科学基金重点联合基金项目1项。申请发明专利9项，获得软件著作权登记8项。
[1] J Liang, M Chen, J Liang. Graph external attention enhanced transformer. In Proceedings of the 41st International Conference on Machine Learning, 2024
[2] 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
[3] Q Yue, J Cui, L Bai, J Liang, J Liang. A zero-shot learning boosting framework via concept-constrained clustering. Pattern Recognition, 2023, 109937
[4] 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
[5] 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
[6] 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
[7] X Guo, W Wei, J Liang, C Dang, J Liang. Metric learning via perturbing hard-to-classify instances. Pattern Recognition, 2022, 132: 108928
[8] K Yao, J Liang, J Liang, M Li, F Cao. Multi-view graph convolutional networks with attention mechanism. Artificial Intelligence, 2022, 307: 103708
[9] 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
[10] T Guo, J Liang, J Liang, GS Xie. Cross-modal propagation network for generalized zero-shot learning. Pattern Recognition Letters, 2022, 159: 125-131
[11] J Liang, P Zhu, C Dang, Q Hu. Semisupervised laplace-regularized multimodality metric learning. IEEE Transactions on Cybernetics, 2022, 52(5): 2955-2967
[12] 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
[13] 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
[14] J Liang, Q Hu, P Zhu, W Wang. Efficient multi-modal geometric mean metric learning. Pattern Recognition, 2018, 75: 188-198
[15] 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
[16] 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
[17] J Liang, Y Han, Q Hu. Semi-supervised image clustering with multi-modal information. Multimedia Systems, 2016, 22(2): 149-160
[18] 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. 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
2. 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
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. 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
5. 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
6. 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
7. 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
8. 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