Home

Biography

Zhiming is currently an assistant professor at the Department of Computer Science, Shanghai University of Finance and Economics. He obtained his Ph.D. in Computer Science from Shanghai Jiao Tong University in 2020. He received his B.S. in Computer Science from the ACM Class at Shanghai Jiao Tong University in 2014.

Zhiming has a broad interest in fundamental techniques and theories of machine learning, including optimization, generalization, generative models, representation learning, learning theory, design principles of neural networks, etc. Ultimately, he hopes to contribute to laying a solid foundation for the application of artificial (general) intelligence in the real world.

Documents & Links

Google Scholar

Github

DBLP

CV

Prospective Students

Selected Publications

Recurrent Temporal Revision Graph Networks.

• Yizhou Chen, Anxiang Zeng, Guangda Huzhang, Qingtao Yu, Kerui Zhang, Cao Yuanpeng, Kangle Wu, Han Yu, Zhiming Zhou.
• The Thirty-seventh Annual Conference on Neural Information Processing Systems (NeurIPS, 2023).
• [arXiv] [OpenReview]

Clustered Embedding Learning for Recommender Systems.

• Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, Heng-yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou.
• The 2023 ACM Web Conference (WWW, 2023).
• [arXiv] [ACM]

Lipschitz Generative Adversarial Nets.

• Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang.
• The Thirty-sixth International Conference on Machine Learning (ICML, 2019).
• [arXiv] [Slide] [Poster] [Code1] [Code2] [Code3]

AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods.

• Zhiming Zhou*, Qingru Zhang*, Guansong Lu, Hongwei Wang, Weinan Zhang, Yong Yu.
• The Seventh International Conference on Learning Representations (ICLR, 2019).
• [arXiv] [OpenReview] [Poster] [Code]

Activation Maximization Generative Adversarial Nets.

• Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Jun Wang, Yong Yu.
• The Sixth International Conference on Learning Representations (ICLR, 2018).
• [arXiv] [OpenReview] [Code1] [Code2]

Sparse-as-Possible SVBRDF Acquisition.

• Zhiming Zhou, Guojun Chen, Yue Dong, David Wipf, Yong Yu, John Snyder, Xin Tong.
• ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia, 2016.
• [PDF] [ACM] [Slide]