Geometric deep learning: going beyond euclidean data
IEEE Signal Processing Magazine, 2017•ieeexplore.ieee.org
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.
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.
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.
ieeexplore.ieee.org