DL Journal Club: “Geometric deep learning on graphs and manifolds” digest by P. Borgnat after M. Bronstein’s talk

Friday, July 13th, 2018 at 1:30pm – IXXI Conference room M7

Pierre Borgnat will be giving a (simplified) presentation of the tutorial “Geometric deep learning on graphs and manifolds” done by Michael Bronstein at the Graph Signal Processing workshop held at EPFL, june 6–8, 2018 . We should prepare for “a mix of Convolutional Neural Networks and Graph Signal Processing”.

There is a tutorial/review article for reference, which can be good to read before the meeting: M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst, Geometric deep learning: going beyond Euclidean data, IEEE Signal Processing Magazine 2017.

Full details on the Journal Club’s repository.

Example Laplacian eigenfunctions on Euclidian and non-Euclidian domains used for Spectral CNN, Bronstein et al. (2017)