General Training Session: Geometric deep learning on graphs and manifolds – going beyond Euclidean data
General Training Session: Geometric deep learning on graphs and manifolds – going beyond Euclidean data


In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data, while many important applications have to deal with non-Euclidean structured data, such as graphs and manifolds. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, and web applications. The adoption of deep learning in these fields has been lagging behind until recently, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. The purpose of the proposed tutorial is to introduce the emerging field of geometric deep learning on graphs and manifolds, overview existing solutions and applications for this class of problems, as well as key difficulties and future research directions.


Federico Monti is a PhD student under the supervision of prof. Michael Bronstein, he moved to Università della Svizzera italiana in 2016 after achieving cum laude his B.Sc. and M.Sc. in Computer Science and Engineering at Politecnico di Milano. His research currently deals with the emerging field of Geometric Deep Learning and, in particular, with generalisation of Convolutional Neural Networks (CNNs) for signals defined on manifolds and graphs.

GDL currently represents one of the latest and most active research fields in Machine Learning thanks to broad applicability and novelty it presents. Possible applications of GDL techniques range from Recommendation Systems (e.g. Matrix Completion problems), to Biomedical solutions (e.g. Disease Predictions), to Shape Analysis approaches (e.g. Shape Correspondence)

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