Statistical Properties of Training & Generalization
Itay Lavie, Noam Levi, Yonatan Kahn
stat.ML cs.LG hep-ph physics.data-an
Abstract
Deep learning has managed to evade numerous intuitions from classical statistics to achieve unprecedented performance on a number of real-world tasks. In this article, we investigate the key features and surprises of deep learning from a physics-informed perspective, taking care to point out and justify where possible the many choices inherent in constructing a deep learning model. In particular, we review the phenomenon of neural scaling laws and discuss their interplay with the constraints and inductive biases which may be present when applying machine learning to problems in physics.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.