# Geometric Deep Learning: Going beyond Euclidean data

“Geometric Deep Learning: Going beyond Euclidean data” (2017), by Michael M. Bronstein et al. Computer Science research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur D. Szlam, Pierre Vandergheynst
- Published: July 1, 2017
- Field: Computer Science
- DOI: https://doi.org/10.1109/msp.2017.2693418
- Free PDF: https://arxiv.org/pdf/1611.08097

## 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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