Beyond Euclidean: Hyperbolic and Hyperspherical Learning for Computer Vision workshop at the European Conference on Computer Vision ECCV 2024

Activity

Workshop with paper proceedings published in Springer via a call for papers

Description

Within deep learning, Euclidean geometry is the default basis for deep neural networks, yet the naive assumption that such a topology is optimal for all data types and tasks does not necessarily hold. There exists a growing body of evidence to suggest that data and the representations that we aim to learn can be better captured through learning in corresponding geometries when exhibiting non-Euclidean structures. The interest in non-Euclidean deep learning has grown dramatically in recent years, with advancing methodologies, libraries, and applications. Beyond Euclidean will be the first workshop solely devoted to deep learning in hyperbolic and hyperspherical spaces.

Period	28 Sept 2024
Event type	Workshop
Location	Milan, ITALYShow on map
Degree of Recognition	International