CORDIS Project
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This project aims to develop a framework for applying deep learning techniques to non-Euclidean data, which is crucial in areas like computer graphics and biomedicine. By addressing the limitations of existing methods, the project seeks to enhance performance in analyzing complex geometric data.
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…
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Partner organizations (coordinator is shown above), with normalized type and CORDIS activity type. Guests see up to 4 partners.
Switzerland, Lugano
Type: University / higher education
Activity type: Higher or Secondary Education Establishments
SME: No
United Kingdom, LONDON
Type: Company (for-profit)
Activity type: Private for-profit entities (excluding Higher or Secondary Education Establishments)
SME: No
IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
United Kingdom, LONDON
Type: University / higher education
Activity type: Higher or Secondary Education Establishments
SME: No
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