Invited talk
in
Workshop: Physics for Machine Learning

Physics-inspired learning on graphs

Michael Bronstein

2023 Invited talk
in
Workshop: Physics for Machine Learning

Abstract

The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a wide range of applications, from particle physics to protein design. From a theoretical viewpoint, it established the link to the Weisfeiler-Lehman hierarchy, allowing to analyse the expressive power of GNNs. We argue that the very “node-and-edge”-centric mindset of current graph deep learning schemes may hinder future progress in the field. As an alternative, we propose physics-inspired “continuous” learning models that open up a new trove of tools from the fields of differential geometry, algebraic topology, and differential equations so far largely unexplored in graph ML.

Speaker

Michael Bronstein

Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales. Michael received his PhD from the Technion in 2007. He has held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with the Institute for Advanced Study at TUM (as a Rudolf Diesel Fellow, 2017-2019) and Harvard (as a Radcliffe fellow, 2017-2018). Michael is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019). He has previously served as Principal Engineer at Intel Perceptual Computing and was one of the key developers of the Intel RealSense technology.

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