Geometric Deep Learning: Going beyond Euclidean data
Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst
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.
Topics & Concepts
Deep learningComputer scienceArtificial intelligenceEuclidean geometryArtificial neural networkComputer graphicsField (mathematics)GraphicsTheoretical computer scienceEuclidean spaceGridKey (lock)Geometric modelingMachine learningMathematicsComputer graphics (images)Computer securityPure mathematicsGeometry3D Shape Modeling and AnalysisGraph Theory and AlgorithmsComputational Geometry and Mesh Generation