Title: Understanding machine learning via exactly solvable models
Speaker: Lenka Zdeborova
Speaker Info: EPFL
Brief Description:
Special Note:

The affinity between statistical physics and machine learning has long history, this is reflected even in the machine learning terminology that is in part adopted from physics. 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 learning algorithm.
Date: Friday, May 21, 2021
Time: 10am
Where: Zoom
Contact Person: Ezra Getzler
Contact email: getzler@northwestern.edu
Contact Phone:
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