Geometric Deep Learning: Going beyond Euclidean data
Michael M. BronsteinIntel Perceptual Computing, IsraelJoan BrunaNYU, Courant InstituteYann LeCunFacebookArthur SzlamFacebook AI Research, Menlo Park, CaliforniaPierre VandergheynstEcole Polytechnique Federale de Lausanne, Switzerland
2017en
ABI
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.
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