Renjie Liao, Department of Computer Science
University of Toronto
Graphs are ubiquitous in many domains like computer vision, natural language processing, computational chemistry, and computational social science. Although deep learning has achieved tremendous success, effectively handling graphs is still challenging due to their discrete and combinatorial structures. In this talk, I will discuss my recent work which improves deep learning on graphs from both modeling and algorithmic perspectives.
First, I will discuss graph representation learning with a focus on how to effectively capture the multi-scale dependencies in the graphs and how to learn a cost function on bipartite graphs using a simple (30 lines of code) differentiable matching algorithm.
Then I will introduce an efficient and scalable deep generative model of graphs. At last, I will present a novel learning algorithm based on the implicit function theorem which efficiently differentiates through convergent dynamic processes on graphs and other types of data.
Bio: Renjie Liao is a PhD candidate in Computer Science at University of Toronto, jointly advised by Richard Zemel and Raquel Urtasun. He is also a Senior Research Scientist at Uber Advanced Technology Group. He obtained his BEng degree in Electrical Engineering from Beihang University and MPhil degree in Computer Science from the Chinese University of Hong Kong. His research interests are broadly in machine learning, computer vision, and self-driving, with a focus on deep learning on graphs.
His work has been recognized by the best paper award (ICML Workshop on Tractable Probabilistic Modeling). He is a recipient of Connaught and RBC fellowships. He has co-organized several workshops on “deep learning on graphs” at top-tier machine learning conferences including NeurIPS 2019, ICML 2019, and KDD 2019.
200 University Avenue West
Waterloo, ON N2L 3G1