[Paper] Geometry-aware similarity metrics for neural representations on Riemannian and statistical manifolds

Summary

This paper introduces Metric Similarity Analysis (MSA), a novel method that uses Riemannian geometry to compare the intrinsic geometry of neural network representations. Unlike existing methods that focus on extrinsic geometry, MSA aims to capture subtle yet crucial distinctions between fundamentally different neural network solutions. This approach promises a deeper understanding of how neural networks solve tasks by analyzing their representational geometries more accurately.

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