Obiettivo The aim of the project is to develop a geometrically meaningful framework that allows generalizing deep learning paradigms to data on non-Euclidean domains. Such geometric data are becoming increasingly important in a variety of fields including computer graphics and vision, sensor networks, biomedicine, genomics, and computational social sciences. Existing methodologies for dealing with geometric data are limited, and a paradigm shift is needed to achieve quantitatively and qualitatively better results. Our project is motivated by the recent dramatic success of deep learning methods in a wide range of applications, which has literally shaken the academic and industrial world. Though these methods have been known for decades, the computational power of modern computers, availability of large datasets, and efficient optimization methods allowed creating and effectively training complex models that made a qualitative breakthrough. In particular, in computer vision, deep neural networks have achieved unprecedented performance on notoriously hard problems such as object recognition. However, so far research has mainly focused on developing deep learning methods for Euclidean data such as acoustic signals, images, and videos. In fields dealing with geometric data, the adoption of deep learning has been lagging behind, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. The ambition of the project is to develop geometric deep learning methods all the way from a mathematical model to an efficient and scalable software implementation, and apply them to some of today’s most important and challenging problems from the domains of computer graphics and vision, genomics, and social network analysis. We expect the proposed framework to lead to a leap in performance on several known tough problems, as well as to allow addressing new and previously unthinkable problems. Campo scientifico natural sciencesphysical sciencestheoretical physicsparticle physicsneutrinosnatural sciencescomputer and information sciencesartificial intelligencecomputer visionnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencesmathematicspure mathematicsgeometrynatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Parole chiave geometric deep learning convolutional neural networks intrinsic geometry shape analysis shape correspondence signal processing on graphs spectral methods face analysis social network analysis Programma(i) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Argomento(i) ERC-2016-COG - ERC Consolidator Grant Invito a presentare proposte ERC-2016-COG Vedi altri progetti per questo bando Meccanismo di finanziamento ERC-COG - Consolidator Grant Istituzione ospitante THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD Contribution nette de l'UE € 200 000,10 Indirizzo WELLINGTON SQUARE UNIVERSITY OFFICES OX1 2JD Oxford Regno Unito Mostra sulla mappa Regione South East (England) Berkshire, Buckinghamshire and Oxfordshire Oxfordshire Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 200 000,10 Beneficiari (4) Classifica in ordine alfabetico Classifica per Contributo netto dell'UE Espandi tutto Riduci tutto THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD Regno Unito Contribution nette de l'UE € 200 000,10 Indirizzo WELLINGTON SQUARE UNIVERSITY OFFICES OX1 2JD Oxford Mostra sulla mappa Regione South East (England) Berkshire, Buckinghamshire and Oxfordshire Oxfordshire Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 200 000,10 UNIVERSITA DELLA SVIZZERA ITALIANA Partecipazione conclusa Svizzera Contribution nette de l'UE € 87 624,90 Indirizzo VIA GIUSEPPE BUFFI 13 6900 Lugano Mostra sulla mappa Regione Schweiz/Suisse/Svizzera Ticino Ticino Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 87 624,90 TWITTER UK LIMITED Partecipazione conclusa Regno Unito Contribution nette de l'UE € 375 000,00 Indirizzo 1ST FLOOR 20 AIR STREET W1B 5AN London Mostra sulla mappa Regione London Inner London — West Westminster Tipo di attività Private for-profit entities (excluding Higher or Secondary Education Establishments) Collegamenti Contatta l’organizzazione Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 375 000,00 IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE Regno Unito Contribution nette de l'UE € 1 335 250,00 Indirizzo SOUTH KENSINGTON CAMPUS EXHIBITION ROAD SW7 2AZ LONDON Mostra sulla mappa Regione London Inner London — West Westminster Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 1 335 250,00