## Article Source

# Geometric Deep Learning

## Abstract

In the last decade, **Deep Learning approaches** (e.g. Convolutional Neural Networks and Recurrent Neural Networks) allowed to achieve unprecedented performance on a broad range of problems coming from a variety of different fields (e.g. Computer Vision and Speech Recognition). Despite the results obtained, research on DL techniques has mainly focused so far on data defined on *Euclidean domains* (i.e. grids). Nonetheless, in a multitude of different fields, such as: Biology, Physics, Network Science, Recommender Systems and Computer Graphics; one may have to deal with data defined on *non-Euclidean domains* (i.e. graphs and manifolds). The adoption of Deep Learning in these particular fields has been lagging behind until very recently, primarily since the non-Euclidean nature of data makes the definition of basic operations (such as convolution) rather elusive. *Geometric Deep Learning* deals in this sense with the extension of Deep Learning techniques to *graph/manifold structured data*.

## Papers and Code

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