Geometric deep learning: going beyond euclidean data

2017

Abstract

Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains, such as graphs and manifolds. The purpose of this article is to overview different examples of geometric deep-learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field.

Authors

Michael Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
A Szlam
Pierre Vandergheynst