Deep LEarning on MANifolds and graphs

Información del proyecto

LEMAN

Identificador del acuerdo de subvención: 724228

DOI

10.3030/724228

Proyecto cerrado

Fecha de la firma de la CE 15 Diciembre 2016

Fecha de inicio 1 Octubre 2017

Fecha de finalización 30 Septiembre 2023

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total

€ 1 997 875,00

Aportación de la UE

€ 1 997 875,00

Coordinado por

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
United Kingdom

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Publicaciones

Book chapters (2)

Unsupervised Diffeomorphic Surface Registration and Non-linear Modelling

Autores: Balder Croquet, Daan Christiaens, Seth M. Weinberg, Michael Bronstein, Dirk Vandermeulen, Peter Claes
Publicado en: Lecture Notes in Computer Science, Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 2021, Página(s) 118-128
Editor: Springer International Publishing
DOI: 10.1007/978-3-030-87202-1_12

The Average Mixing Kernel Signature

Autores: Luca Cosmo, Giorgia Minello, Michael Bronstein, Luca Rossi, Andrea Torsello
Publicado en: Lecture Notes in Computer Science, Computer Vision – ECCV 2020, 2021, Página(s) 1-17
Editor: Springer International Publishing
DOI: 10.1007/978-3-030-58565-5_1

Conference proceedings (24)

Geometrically Principled Connections in Graph Neural Networks

Autores: Shunwang Gong, Mehdi Bahri, Michael M. Bronstein, Stefanos Zafeiriou
Publicado en: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Edición 31, 2022, Página(s) 11412-11421
Editor: IEEE
DOI: 10.1109/cvpr42600.2020.01143

Beltrami Flow and Neural Diffusion on Graphs

Autores: Chamberlain, BP; Rowbottom, J; Eynard, D; Di Giovanni, F; Dong, X; Bronstein, MM
Publicado en: NeurIPS, 2021
Editor: neurips
DOI: 10.48550/arxiv.2110.09443

Understanding over-squashing and bottlenecks on graphs via curvature

Autores: Topping, J; Di Giovanni, F; Chamberlain, BP; Dong, X; Bronstein, M
Publicado en: ICLR, 2022
Editor: ICLR
DOI: 10.48550/arxiv.2111.14522

PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks

Autores: Svoboda, Jan; Masci, Jonathan; Monti, Federico; Bronstein, Michael M.; Guibas, Leonidas
Publicado en: ICLR, Edición 6, 2019
Editor: ICLR

Edge Directionality Improves Learning on Heterophilic Graphs

Autores: Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael Bronstein
Publicado en: LoG, 2023
Editor: log

Gradient Gating for Deep Multi-Rate Learning on Graphs

Autores: Rusch, T. Konstantin; Chamberlain, Benjamin P.; Mahoney, Michael W.; Bronstein, Michael; Mishra, Siddhartha
Publicado en: ICML, 2023
Editor: icml
DOI: 10.48550/arxiv.2210.00513

DRew: Dynamically Rewired Message Passing with Delay

Autores: Benjamin Gutteridge, Xiaowen Dong, Michael Bronstein, Francesco Di Giovanni
Publicado en: ICML, 2023
Editor: icml

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

Autores: Bodnar, C; Di Giovanni, F; Chamberlain, BP; Liò, P; Bronstein, M
Publicado en: NeurIPS, 2022
Editor: neurips
DOI: 10.48550/arxiv.2202.04579

On Over-Squashing in Message Passing Neural Networks:The Impact of Width, Depth, and Topology

Autores: Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael Bronstein
Publicado en: ICML, 2023
Editor: icml

SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator

Autores: Shunwang Gong, Lei Chen, Michael Bronstein, Stefanos Zafeiriou
Publicado en: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2022
Editor: IEEE
DOI: 10.1109/iccvw.2019.00509

Weisfeiler and Lehman Go Cellular: CW Networks

Autores: Bodnar, Cristian; Frasca, Fabrizio; Otter, Nina; Wang, Yu Guang; Liò, Pietro; Montúfar, Guido; Bronstein, Michael
Publicado en: NeurIPS, 2021
Editor: neurips
DOI: 10.48550/arxiv.2106.12575

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

Graph Neural Networks for Link Prediction with Subgraph Sketching

Autores: Chamberlain, Benjamin Paul; Shirobokov, Sergey; Rossi, Emanuele; Frasca, Fabrizio; Markovich, Thomas; Hammerla, Nils; Bronstein, Michael M.; Hansmire, Max
Publicado en: ICLR, Edición 7, 2023
Editor: icml
DOI: 10.48550/arxiv.2209.15486

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

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Autores: Frasca, F; Bevilacqua, B; Bronstein, M; Maron, H
Publicado en: NeurIPS, 2022
Editor: Neurips
DOI: 10.48550/arxiv.2206.11140

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

Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

Autores: Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Stefanos Zafeiriou, Michael Bronstein
Publicado en: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2022
Editor: IEEE
DOI: 10.1109/iccv.2019.00731

Equivariant Subgraph Aggregation Networks

Autores: Bevilacqua, Beatrice; Frasca, Fabrizio; Lim, Derek; Srinivasan, Balasubramaniam; Cai, Chen; Balamurugan, Gopinath; Bronstein, Michael M.; Maron, Haggai
Publicado en: ICLR, 2022
Editor: iclr
DOI: 10.48550/arxiv.2110.02910

Peer reviewed articles (18)

Nonisometric Surface Registration via Conformal Laplace–Beltrami Basis Pursuit

Autores: Stefan C. Schonsheck, Michael M. Bronstein, Rongjie Lai
Publicado en: Journal of Scientific Computing, Edición 86, 2021, ISSN 0885-7474
Editor: Kluwer Academic/Plenum Publishers
DOI: 10.1007/s10915-020-01390-y

Predicting anticancer hyperfoods with graph convolutional networks

Autores: Guadalupe Gonzalez, Shunwang Gong, Ivan Laponogov, Michael Bronstein, Kirill Veselkov
Publicado en: Human Genomics, Edición 15, 2023, ISSN 1479-7364
Editor: Springer Nature
DOI: 10.1186/s40246-021-00333-4

Alzheimer’s disease: using gene/protein network machine learning for molecule discovery in olive oil

Autores: Luís Rita, Natalie R. Neumann, Ivan Laponogov, Guadalupe Gonzalez, Dennis Veselkov, Domenico Pratico, Reza Aalizadeh, Nikolaos S. Thomaidis, David C. Thompson, Vasilis Vasiliou, Kirill Veselkov
Publicado en: Human Genomics, Edición 17, 2023, ISSN 1479-7364
Editor: Springer Nature
DOI: 10.1186/s40246-023-00503-6

Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

Autores: Alexander A. Aksenov, Ivan Laponogov, Zheng Zhang, Sophie L. F. Doran, Ilaria Belluomo, Dennis Veselkov, Wout Bittremieux, Louis Felix Nothias, Mélissa Nothias-Esposito, Katherine N. Maloney, Biswapriya B. Misra, Alexey V. Melnik, Aleksandr Smirnov, Xiuxia Du, Kenneth L. Jones, Kathleen Dorrestein, Morgan Panitchpakdi, Madeleine Ernst, Justin J. J. van der Hooft, Mabel Gonzalez, Chiara Carazzone,
Publicado en: Nature Biotechnology, Edición 39, 2023, Página(s) 169-173, ISSN 1087-0156
Editor: Nature Publishing Group
DOI: 10.1038/s41587-020-0700-3

Differentiable Graph Module (DGM) for Graph Convolutional Networks

Autores: Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein
Publicado en: IEEE Transactions on Pattern Analysis and Machine Intelligence, Edición 45, 2023, Página(s) 1606-1617, ISSN 0162-8828
Editor: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpami.2022.3170249

De novo design of protein interactions with learned surface fingerprints

Autores: Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, Casper Goverde, Priscilla Turelli, Charlène Raclot, Alexandra Teslenko, Martin Pacesa, Stéphane Rosset, Sandrine Georgeon, Jane Marsden, Aaron Petruzzella, Kefang Liu, Zepeng Xu, Yan Chai, Pu Han, George F. Gao, Elisa Oricchio,
Publicado en: Nature, Edición 617, 2023, Página(s) 176-184, ISSN 0028-0836
Editor: Nature Publishing Group
DOI: 10.1038/s41586-023-05993-x

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

Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19

Autores: Ivan Laponogov, Guadalupe Gonzalez, Madelen Shepherd, Ahad Qureshi, Dennis Veselkov, Georgia Charkoftaki, Vasilis Vasiliou, Jozef Youssef, Reza Mirnezami, Michael Bronstein, Kirill Veselkov
Publicado en: Human Genomics, Edición 15, 2024, ISSN 1479-7364
Editor: Springer Media
DOI: 10.1186/s40246-020-00297-x

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods

Autores: Kirill Veselkov, Guadalupe Gonzalez, Shahad Aljifri, Dieter Galea, Reza Mirnezami, Jozef Youssef, Michael Bronstein, Ivan Laponogov
Publicado en: Scientific Reports, Edición 9, 2022, ISSN 2045-2322
Editor: Nature Publishing Group
DOI: 10.1038/s41598-019-45349-y

Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation

Autores: Mehdi Bahri, Eimear O’ Sullivan, Shunwang Gong, Feng Liu, Xiaoming Liu, Michael M. Bronstein, Stefanos Zafeiriou
Publicado en: International Journal of Computer Vision, Edición 129, 2023, Página(s) 2680-2713, ISSN 0920-5691
Editor: Kluwer Academic Publishers
DOI: 10.1007/s11263-021-01494-4

Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient

Autores: Joshua Southern, Guadalupe Gonzalez, Pia Borgas, Liam Poynter, Ivan Laponogov, Yoyo Zhong, Reza Mirnezami, Dennis Veselkov, Michael Bronstein, Kirill Veselkov
Publicado en: Scientific Reports, Edición 13, 2023, ISSN 2045-2322
Editor: Nature Publishing Group
DOI: 10.1038/s41598-023-41833-8

Other (9)

Learning Interpretable Disease Self-Representations for Drug Repositioning

Autores: Frasca, F; Galeano, D; Gonzalez, G; Laponogov, I; Veselkov, K; Paccanaro, A; Bronstein, MM
Publicado en: arxiv, Edición 21, 2019
Editor: arxiv
DOI: 10.48550/arxiv.1909.06609

Decoding Surface Fingerprints for Protein-Ligand Interactions

Autores: Ilia Igashov, Arian R. Jamasb, Ahmed Sadek, Freyr Sverrisson, Arne Schneuing, Pietro Liò, Tom L. Blundell, Michael Bronstein, Bruno Correia
Publicado en: bioarxiv, 2022
Editor: Cold Spring Harbor Laboratory
DOI: 10.1101/2022.04.26.489341

Unsupervised Network Embedding Beyond Homophily

Autores: Zhong, Zhiqiang; Gonzalez, Guadalupe; Grattarola, Daniele; Pang, Jun
Publicado en: arxiv, 2022
Editor: arxiv
DOI: 10.48550/arxiv.2203.10866

Combinatorial prediction of therapeutic perturbationsusing causally-inspired neural networks

Autores: Guadalupe Gonzalez, Isuru Herath, Kirill Veselkov, Michael Bronstein, Marinka Zitnik
Publicado en: bioarxiv, 2024
Editor: bioarxiv

Structure-based Drug Design with Equivariant Diffusion Models

Autores: Schneuing, Arne; Du, Yuanqi; Harris, Charles; Jamasb, Arian; Igashov, Ilia; Du, Weitao; Blundell, Tom; Lió, Pietro; Gomes, Carla; Welling, Max; Bronstein, Michael; Correia, Bruno
Publicado en: bioarxiv, 2022
Editor: bioarxiv
DOI: 10.48550/arxiv.2210.13695

Fake News Detection on Social Media using Geometric Deep Learning

Autores: Monti, Federico; Frasca, Fabrizio; Eynard, Davide; Mannion, Damon; Bronstein, Michael M.
Publicado en: arxiv, Edición 10, 2019
Editor: arxiv
DOI: 10.48550/arxiv.1902.06673

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

Heterogeneous manifolds for curvature-aware graph embedding

Autores: Di Giovanni, Francesco; Luise, Giulia; Bronstein, Michael
Publicado en: arxiv, 2022
Editor: arxiv
DOI: 10.48550/arxiv.2202.01185

Derechos de propiedad intelectual

Patent (1)

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