Geometric Deep Learning: Going beyond Euclidean data

Michael M. Bronstein, Joan Bruna, Yann Lecun, Arthur Szlam, Pierre Vandergheynst

Research output: Contribution to journalReview article

Abstract

Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them.

Original languageEnglish (US)
Article number7974879
Pages (from-to)18-42
Number of pages25
JournalIEEE Signal Processing Magazine
Volume34
Issue number4
DOIs
StatePublished - Jul 2017

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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    Bronstein, M. M., Bruna, J., Lecun, Y., Szlam, A., & Vandergheynst, P. (2017). Geometric Deep Learning: Going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4), 18-42. [7974879]. https://doi.org/10.1109/MSP.2017.2693418