Understanding Machine Learning via Exactly Solvable Statistical Physics Models
Schedule
Wednesday, February 8, 2023
05:00 UTC
Lenka Zdeborová
EPFL
Host: van Vreeswijk TNS
Recording
Open recording
Event Information
Recording
Available
Host
van Vreeswijk TNS
Duration
70 minutes
Abstract
The affinity between statistical physics and machine learning has a long history. I will describe the main lines of this long-lasting friendship in the context of current theoretical challenges and open questions about deep learning. Theoretical physics often proceeds in terms of solvable synthetic models, I will describe the related line of work on solvable models of simple feed-forward neural networks. I will highlight a path forward to capture the subtle interplay between the structure of the data, the architecture of the network, and the optimization algorithms commonly used for learning.
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