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Geometric Deep Learning: Going beyond Euclidean data | IEEE Journals & Magazine | IEEE Xplore
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Geometric Deep Learning: Going beyond Euclidean data


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 ...Show More

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.
Published in: IEEE Signal Processing Magazine ( Volume: 34, Issue: 4, July 2017)
Page(s): 18 - 42
Date of Publication: 11 July 2017

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