Federico MontiFederico 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).
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (2017)
Ron Levie*, Federico Monti*, Xavier Bresson, Michael M Bronstein
Geometric matrix completion with recurrent multi-graph neural networks (2017)
Federico Monti, Michael M. Bronstein, Xavier Bresson
Geometric deep learning on graphs and manifolds using mixture model CNNs (2016)
Federico Monti*, Davide Boscaini*, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein
In Proc. CVPR2017 (oral presentation)
Deep convolutional neural networks for pedestrian detection (2015)
Denis Tomè*, Federico Monti*, Luca Baroffio, Luca Bondi, Marco Tagliasacchi, Stefano Tubaro
Journal of Signal Processing: Image Communication