Geometric Deep Learning

Geometric Deep Learning is one of the most emerging fields of the Machine Learning community. This website represents a collection of materials of this particular research area.

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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.
This website represents a collection of materials in the field of Geometric Deep Learning. We collect workshops, tutorials, publications and code, that several differet researchers has produced in the last years. Our goal is to provide a general picture of this new and emerging field, which is rapidly developing in the scientific community, thanks to the broad applicability it presents.

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3D Deep Learning

CVPR Tutorial, Honolulu, 21 July 2017

Geometric Deep Learning on Graphs

CVPR Tutorial, Honolulu, 21 July 2017

Geometric Deep Learning on Graphs and Manifolds

Short Course, TU Munich, June-July 2017

Part 1, Part 2

Machine Learning Meets Geometry

SGP Tutorial, London, June 2017

Geometric Deep Learning

SIGGRAPH Asia Tutorial, Macao, December 2016

Geometric Deep Learning

ECCV Tutorial, Amsterdam, October 2016

Deep Learning for Shape Analysis

EUROGRAPHICS Tutorial, Lisbon, May 2016

Papers & Code

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Federico Monti
Università della Svizzera italiana