In the past decade, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. However, so far research has mainly focused on developing deep learning methods for Euclideanstructured data. However, many important applications have to deal with nonEuclidean 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 nonEuclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. This website is a collection of materials in the emerging field of geometric deep learning on graphs and manifolds.
Conferences and Workshops
 IPAM Workshop on New Deep Learning Techniques, UCLA, 59 February 2018
 ICCV Second Workshop Geometry Meets Deep Learning, Venice, 28 October 2017
 ECCV First Workshop Geometry Meets Deep Learning, Amsterdam, 9 October 2016
Tutorials and Short Courses

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, JuneJuly 2017
Video recording 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
Bibliography
 M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst, Geometric deep learning: going beyond Euclidean data, IEEE Signal Processing Magazine 2017 (Review paper)
 R. Levie, F. Monti, X. Bresson, M. M. Bronstein, CayleyNets: Graph convolutional neural networks with complex rational spectral filters, 2017 (CayleyNet framework)
 F. Monti, X. Bresson, M. M. Bronstein, Geometric matrix completion with recurrent multigraph neural networks, 2017 (CNNs on multiple graphs)
 L. Yi, H. Su, X. Guo, L. Guibas,
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
, CVPR 2017 (spectral transformer networks)
 F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, M. M. Bronstein, Geometric deep learning on graphs and manifolds using mixture model CNNs, CVPR 2017 (MoNet framework)
 T. Kipf, M. Welling, Semisupervised Classification with Graph Convolutional Networks, ICLR 2017 (simplification of ChebNet)
 M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NIPS 2017 (ChebNet framework)
 D. Boscaini, J. Masci, E. Rodolà, M. M. Bronstein, Learning shape correspondence with anisotropic convolutional neural networks, NIPS 2016 (Anisotropic CNN framework)
 J. Masci, D. Boscaini, M. M. Bronstein, P. Vandergheynst, Geodesic convolutional neural networks on Riemannian manifolds, 3dRR 2015 (Geodesic CNN framework)
 D. Duvenaud, D. Maclaurin, J. AguileraIparraguirre, R. GomezBombarelli, T. Hirzel, A. AspuruGuzik, R. P. Adams, Convolutional Networks on Graphs for Learning Molecular Fingerprints, NIPS 2015 (molecular fingerprints using graph CNNs)
 J. Atwood, D. Towsley, DiffusionConvolutional Neural Networks, 2015
 M. Henaff, J. Bruna, Y. LeCun: Deep Convolutional Networks on GraphStructured Data, 2015
 J. Bruna, W. Zaremba, A. Szlam, Y. LeCun, Spectral Networks and Deep Locally Connected Networks on Graphs, ICLR 2014 (spectral CNN on graphs)
 F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, G. Monfardini,
The graph neural network model,
Trans. Neural Networks 20(1):6180, 2009 (first neural networks on graphs)