
曾俊桦,博士,讲师。2024年6月博士毕业于广东工业大学自动化学院,师从人工智能及信号处理领域专家赵启斌教授和周郭许教授(国家“万人计划”科技创新领军人才)。已在International Conference on Machine Learning(ICML)、IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR)、Neural Networks等国际顶级人工智能会议(CCF A类)与期刊上以发表多篇论文。
Email:jh.zenggdut@gmail.com
办公地点:太阳成集团tyc122cc411
研究方向:张量机器学习与大数据分析、模式识别与图像处理、大语言模型
教育经历:
2020.09-2024.06广东工业大学|控制科学与工程|博士
2018.09-2020.06广东工业大学|控制科学与工程|硕士
2014.09-2018.06广东工业大学|自动化|学士
科研与学术工作经历:
(1)2025.05-至今,122大阳城集团网站,太阳成集团tyc122cc,讲师
(2)2023.05-2024.04,日本理化学研究所(RIKEN),革新智能综合研究中心(AIP),研究实习员
代表性学术论文:
[1].J. Zeng*, C. Li*, Z. Sun, Q. Zhao, and G. Zhou, tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs), in International Conference on Machine Learning (ICML), PMLR, 2024, pp. 58329-58347(CCF A类会议)
[2].C. Li*,J. Zeng*, Z. Tao, and Q. Zhao, Permutation search of tensor network structures via local sampling, in International Conference on Machine Learning (ICML), PMLR, 2022, pp. 13106--13124(CCF A类会议)
[3].J. Zeng, G. Zhou, Y. Qiu, C. Li, and Q. Zhao, Bayesian tensor network structure search and its application to tensor completion, Neural Networks, vol. 175, p. 106290, 2024(SCI一区TOP期刊)
[4].J. Zeng, G. Zhou, Y. Qiu, Y. Ma, and Q. Zhao, Hyperspectral and Multispectral Image Fusion via Bayesian Nonlocal CP Factorization, IEEE Geoscience and Remote Sensing Letters, 2023(SCI二区)
[5].C. Li,J. Zeng*, C. Li*, C. F. Caiafa, and Q. Zhao, Alternating local enumeration (tnale): Solving tensor network structure search with fewer evaluations, in International Conference on Machine Learning (ICML), PMLR, 2023, pp. 20384--20411(CCF A类会议)
[6].Y.-B. Zheng, X.-L. Zhao,J. Zeng, et al., SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 26254--26263(CCF A类会议)
发明专利:
[1].一种对抗生成网络的处理方法和装置,CN110852424B,发明人:曾俊桦,赵启斌,周郭许。
[2].一种基于皮肤特性评价的护肤产品的推荐方法及系统,CN107480719B,发明人:曾俊桦,李东。
科研项目:
[1].国家自然科学基金面上项目:基于张量网络表征的机器学习理论与算法研究,项目编号:62071132,2021.01-2024.12,参与。