ERC Starting Grant · 2021
Computational Hardness Of RepresentAtion Learning
Rich internal representations of complex data are crucial to the predictive power of neural networks. Unfortunately, current statistical analyses are restricted to over-simplified networks, whose representations (i.e., weight matrices) are either random, and/or project the data in comparatively very large or very low dimensional spaces; in many applications the situation is very different. The modelisation of realistic data is another issue. There is an urgent need to reconcile theory and practice. Based on a synergy of the mathematical physics of spin glasses, matrix-models from physics, and information and random matrix theory, CHORAL’s statistical framework will delimit computational…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.