Deep LEarning on MANifolds and graphs

Publicaciones

Geometric Deep Learning: Going beyond Euclidean data

Autores: Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst
Publicado en: IEEE Signal Processing Magazine, Edición 34/4, 2017, Página(s) 18-42, ISSN 1053-5888
Editor: Institute of Electrical and Electronics Engineers
DOI: 10.1109/msp.2017.2693418

Interactive curve constrained functional maps

Autores: A. Gehre, M. M. Bronstein, L. Kobbelt, J. Solomon
Publicado en: Computer Graphics Forum, 2018, ISSN 1467-8659
Editor: Wiley
DOI: 10.1111/cgf.13486

Kernel functional maps

Autores: L. Wang, A. Gehre, M. M. Bronstein, J. Solomon
Publicado en: Computer Graphics Forum, 2018, ISSN 1467-8659
Editor: Wiley
DOI: 10.1111/cgf.13488

Improved Functional Mappings via Product Preservation

Autores: D. Nogneng, S. Melzi, E. Rodolà, U. Castellani, M. Bronstein, M. Ovsjanikov
Publicado en: Computer Graphics Forum, Edición 37/2, 2018, Página(s) 179-190, ISSN 0167-7055
Editor: Blackwell Publishing Inc.
DOI: 10.1111/cgf.13352

Functional Maps Representation On Product Manifolds

Autores: E. Rodolà, Z. Lähner, A. M. Bronstein, M. M. Bronstein, J. Solomon
Publicado en: Computer Graphics Forum, Edición 38/1, 2018, Página(s) 678-689, ISSN 0167-7055
Editor: Blackwell Publishing Inc.
DOI: 10.1111/cgf.13598

CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters

Autores: Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein
Publicado en: IEEE Transactions on Signal Processing, Edición 67/1, 2019, Página(s) 97-109, ISSN 1053-587X
Editor: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tsp.2018.2879624

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

Autores: P. Gainza, F. Sverrisson, F. Monti, E. Rodolà, D. Boscaini, M. M. Bronstein, B. E. Correia
Publicado en: Nature Methods, 2020, ISSN 1548-7091
Editor: Nature Publishing Group
DOI: 10.1038/s41592-019-0666-6

Using attribution to decode binding mechanism in neural network models for chemistry

Autores: Kevin McCloskey, Ankur Taly, Federico Monti, Michael P. Brenner, Lucy J. Colwell
Publicado en: Proceedings of the National Academy of Sciences, 2018, Página(s) 201820657, ISSN 0027-8424
Editor: National Academy of Sciences
DOI: 10.1073/pnas.1820657116

Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

Autores: Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, Michael M. Bronstein
Publicado en: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, Página(s) 5425-5434, ISBN 978-1-5386-0457-1
Editor: IEEE
DOI: 10.1109/cvpr.2017.576

Geometric matrix completion with recurrent multi-graph neural networks

Autores: F. Monti, M. M. Bronstein, X. Bresson
Publicado en: Neural Information Processing Systems, 2017
Editor: NIPS

Efficient Deformable Shape Correspondence via Kernel Matching

Autores: Matthias Vestner, Zorah Lahner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodola, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers
Publicado en: 2017 International Conference on 3D Vision (3DV), 2017, Página(s) 517-526, ISBN 978-1-5386-2610-8
Editor: IEEE
DOI: 10.1109/3dv.2017.00065

Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

Autores: Or Litany, Tal Remez, Emanuele Rodola, Alex Bronstein, Michael Bronstein
Publicado en: 2017 IEEE International Conference on Computer Vision (ICCV), 2017, Página(s) 5660-5668, ISBN 978-1-5386-1032-9
Editor: IEEE
DOI: 10.1109/iccv.2017.603

MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS

Autores: Federico Monti, Karl Otness, Michael M. Bronstein
Publicado en: 2018 IEEE Data Science Workshop (DSW), 2018, Página(s) 225-228, ISBN 978-1-5386-4410-2
Editor: IEEE
DOI: 10.1109/dsw.2018.8439897

Graph Neural Networks for IceCube Signal Classification

Autores: Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat Prabhat, Wahid Bhimji, Michael Bronstein, Spencer Klein, Joan Bruna
Publicado en: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, Página(s) 386-391, ISBN 978-1-5386-6805-4
Editor: IEEE
DOI: 10.1109/icmla.2018.00064

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

Autores: Monti, Federico; Bronstein, Michael M.; Bresson, Xavier
Publicado en: NIPS, Edición 7, 2017
Editor: Nips

PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks

Autores: Svoboda, Jan; Masci, Jonathan; Monti, Federico; Bronstein, Michael M.; Guibas, Leonidas
Publicado en: ICLR, Edición 7, 2019
Editor: ICLR

Partition and Code: Learning how to compress graphs

Autores: Giorgos Bouritsas; Loukas, A.; Karalias, N.; Bronstein, M. M.
Publicado en: Neurips, Edición 1, 2021
Editor: Neurips
DOI: 10.48550/arxiv.2107.01952

GRAND: Graph Neural Diffusion

Autores: Chamberlain, Benjamin Paul; Rowbottom, James; Gorinova, Maria; Webb, Stefan; Rossi, Emanuele; Bronstein, Michael M.
Publicado en: ICML, Edición 9, 2021
Editor: ICML
DOI: 10.48550/arxiv.2106.10934

Fake News Detection on Social Media using Geometric Deep Learning

Autores: Monti, Federico; Frasca, Fabrizio; Eynard, Davide; Mannion, Damon; Bronstein, Michael M.
Publicado en: Edición 2, 2019
Editor: arxiv

Dual-Primal Graph Convolutional Networks

Autores: Monti, Federico; Shchur, Oleksandr; Bojchevski, Aleksandar; Litany, Or; Günnemann, Stephan; Bronstein, Michael M.
Publicado en: Edición 5, 2018
Editor: arxiv

Derechos de propiedad intelectual

System and a method for learning features on geometric domains

Número de solicitud/publicación: us 10210430
Fecha: 2016-01-22
Solicitante(s): UNIVERSITA DELLA SVIZZERA ITALIANA

System and a method for learning features on geometric domains

Número de solicitud/publicación: us 10210430
Fecha: 2016-01-22
Solicitante(s): UNIVERSITA DELLA SVIZZERA ITALIANA

System and a method for learning features on geometric domains

Número de solicitud/publicación: us 10210430
Fecha: 2016-01-22
Solicitante(s): UNIVERSITA DELLA SVIZZERA ITALIANA

System and a method for learning features on geometric domains

Número de solicitud/publicación: us 10210430
Fecha: 2016-01-22
Solicitante(s): UNIVERSITA DELLA SVIZZERA ITALIANA

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Publicaciones

Derechos de propiedad intelectual

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