Understanding Machine Learning via Exactly Solvable Statistical Physics Models
Event details
| Date | 03.05.2023 |
| Hour | 12:15 › 13:45 |
| Speaker | Lenka Zdeborová |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
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.
Practical information
- Informed public
- Free
Organizer
- João Penedones
Contact
- Corinne Weibel