Exploiting Graph Invariants in Deep Learning

Speaker: Marc Lelarge, Inria

Bio

Dr. Marc Lelarge is a researcher at INRIA. He is also a lecturer in deep learning at Ecole Polytechnique (Palaiseau, France) and Ecole Normale Superieure. He graduated from Ecole Polytechnique, qualified as an engineer at Ecole Nationale Superieure des Telecommunications (Paris) and received a PhD in Applied Mathematics from Ecole Polytechnique in 2005. Recipient of the 2012 SIGMETRICS rising star researcher award and the 2015 Best Publication in Applied Probability Award with Mohsen Bayati and Andrea Montanari for their work on compressed sensing.

Abstract

Geometric deep learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. In this talk, I will present some advances of geometric deep learning applied to combinatorial structures. I will focus on various classes of graph neural networks that have been shown to be successful in a wide range of applications with graph structured data.