Free Decompression with Algebraic Spectral Curves
Tools from random matrix theory have become central to deep learning theory, using spectral information to provide mechanisms for modeling generalization, robustness, scaling, and failure modes. While often capable of modeling empirical behavior, practical computations are limited by matrix size, often imposing a restriction to models that are too small to be realistic. This motivates the inference of properties of larger models from the behavior of smaller ones. Free decompression (FD) is a recently proposed method for extrapolating spectral information across matrix sizes, but its utility is currently limited by strong assumptions that preclude its implementation on more realistic machi...
| Authors | Siavash Ameli, Chris van der Heide, Liam Hodgkinson et al. |
|---|---|
| Primary category | stat.ML |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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