RG-inspired theory says neural nets generalize by capturing scale-invariant data structure
burny_tech · x · 2026-07-29
The post argues that neural networks generalize because natural data share scale-invariant correlation structures, not because training somehow “magically” avoids memorization.
- The core claim is that optimization layers act like a coarse-grained view of the data’s correlation structure.
- As training converges, layers capture recurring patterns across scales.
- If test data come from the same universality class, the learned representation still matches after rescaling.
- The image frames this as an RG-inspired explanation: generalization emerges when train and test data preserve the same scale-invariant structure.
Related event: Explaining Neural Network Generalization via Renormalization Group(2 posts)→
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