最终学位：博士
导师类型：硕士生导师
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、大数据分析技术、人工智能
梁建青，博士，副教授，硕士生导师。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十余篇。获天津大学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7项，获得软件著作权登记6项。
[1] Q Yue, J Cui, L Bai, J Liang, J Liang. A zero-shot learning boosting framework via concept-constrained clustering. Pattern Recognition, 2023, 109937.
[2] 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.
[3] 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.
[4] 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.
[5] X Guo, W Wei, J Liang, C Dang, J Liang. Metric learning via perturbing hard-to-classify instances. Pattern Recognition, 2022, 132, 108928.
[6] K Yao, J Liang, J Liang, M Li, F Cao. Multi-view graph convolutional networks with attention mechanism. Artificial Intelligence, 2022, 307, 103708.
[7] 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.
[8] T Guo, J Liang, J Liang, GS Xie. Cross-modal propagation network for generalized zero-shot learning. Pattern Recognition Letters, 2022, 159, 125-131.
[9] J Liang, P Zhu, C Dang, Q Hu. Semisupervised laplace-regularized multimodality metric learning. IEEE Transactions on Cybernetics, 2022, 52(5): 2955-2967.
[10] 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.
[11] 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.
[12] J Liang, Q Hu, P Zhu, W Wang. Efficient multi-modal geometric mean metric learning. Pattern Recognition, 2018, 75:188–198.
[13] 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.
[14] 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.
[15] J Liang, Y Han, Q Hu. Semi-supervised image clustering with multi-modal information. Multimedia Systems, 2016, 22(2): 149–160.
[16] 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