Neural Graph Matching and Beyond


In this talk, I will first give a brief introduction on graph matching, which is a combinatorial problem in nature. Then we will show two deep network based pipelines for addressing the graph matching problem via deep learning. The models involve learning of the association based graph node embedding, cross-graph affinity learning, and a Sinkhorn layer for solving the linear assignment task, etc. We will also discuss some works on joint matching and link prediction among two or multiple graphs. In the end, some discussion will be given on the future work and outlook for connecting graph matching with machine learning.


Prof. Junchi YAN
Shanghai Jiao Tong University

Date & Time

14 Aug 2019 (Wednesday) 9:00 - 9:45


E11-G015 (University of Macau)

Organized by

Department of Computer and Information Science


Dr. Junchi Yan is currently an Independent Research Professor (PhD Advisor) with the Department of Computer Science and Engineering, Shanghai Jiao Tong University. He is also affiliated with The Artificial Intelligence Institute of SJTU and an adjunct professor with the School of Data Science, Fudan University. Before that, he was a Research Staff Member with IBM Research - China where he started his career since April 2011. He obtained a Ph.D. at the Department of Electronic Engineering from Shanghai Jiao Tong University, China. His work on graph matching received the ACM China Doctoral Dissertation Nomination Award and China Computer Federation Doctoral Dissertation Award. His research interests are machine learning, data mining and computer vision. He serves as an Associate Editor for IEEE ACCESS, (Managing) Guest Editor for IEEE Transactions on Neural Network and Learning Systems, Pattern Recognition Letters, Pattern Recognition, Vice Secretary of China CSIG-BVD Technical Committee, and on the executive board of ACM China Multimedia Chapter. He has published 50+ peer reviewed papers in top venues in AI and has filed 20+ US patents. He has won the Distinguished Young Scientist of Scientific Chinese for year 2018.