[Paper] From Data Statistics to Feature Geometry: How Correlations Shape Superposition

Summary

This paper explores how feature correlations, moving beyond idealized sparse and uncorrelated settings, influence superposition in neural networks. It aims to provide a more realistic understanding of how neural networks represent an over-complete basis, a central idea in mechanistic interpretability. This research could refine existing theories and impact dictionary learning approaches like sparse autoencoders.

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