【学术会议】Representation Learning for Graphs and Dynamic Graphs

 主题: Representation Learning for Graphs and Dynamic Graphs

 报告人: Prof. SONG Le, Georgia Tech

时间: 10:00 am – 11:00 am, March 8, 2019

地点: Boardroom, Dao Yuan Building

摘要：

Graphs and dynamic graphs have become a universal language in science and technology for describing structures in data, modeling complex systems, and expressing symbolic knowledges. How to represent complex graphs, such that models and algorithms over graphs can become more effective? In this talk, I will describe a learning framework for graph representation by neuralizing message passing operators. I will show that the learned representation can be thousands of times more compact than hand-crafted features, and such representation can be learned efficiently for very large graphs and used with reinforcement learning for solving challenging combinatorial optimization problems. In the end, I will also explain how to understand such deep representation over graphs.

简介：

Le Song is an Associate Professor in the College of Computing, and an Associate Director of the Center for Machine Learning, Georgia Institute of Technology. He is also a Principal Engineer of Ant Financial, Alibaba. Before he joined Georgia Institute of Technology in 2011, he was postdoc in the Department of Machine Learning, Carnegie Mellon University, and a research scientist at Google. His principal research direction is machine learning, especially nonlinear models, such as kernel methods and deep learning, probabilistic graphical models, and optimization. He is the recipient of the NSF CAREER Award’14, and many best paper awards, including the NIPS’17 Materials Science Workshop Best Paper Award, the Recsys’16 Deep Learning Workshop Best Paper Award, AISTATS'16 Best Student Paper Award, IPDPS'15 Best Paper Award, , NIPS’13 Outstanding Paper Award, and ICML’10 Best Paper Award.  He served as the area chair or senior program committee for many leading machine learning and AI conferences such as ICML, NIPS, AISTATS, AAAI and IJCAI, and the action editor for JMLR and IEEE TPAMI.