[finished]ipi seminar 17:00-18:30, Wednesday June 9, 2021
知の物理学研究センター / Institute for Physics of Intelligence (ipi)
【Date】June 9 (Wednesday), 17:00-18:30JST
【Title】"Understanding machine learning via exactly solvable models"
The affinity between statistical physics and machine learning has a 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.