Explaining Neural Network Generalization via Renormalization Group
A recent perspective uses the Renormalization Group (RG) to explain neural network generalization, suggesting that models learn scale-invariant correlation structures in natural data rather than simply memorizing training samples.
2026-07-29 ~ 2026-07-29 · 2 related posts
- RG-inspired theory says neural nets generalize by capturing scale-invariant data structure — burny_tech · 2026-07-29
- RG view of generalization says neural nets learn scale-invariant correlation structure — burny_tech · 2026-07-29