SOURCE-LINKED INTELLIGENCE

Symmetries and Singularities

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Deep neural networks are highly over-parameterized, and different parameter values represent the same predictive function. This makes their effective complexity difficult to measure using only the number of parameters or the rank of the Hessian. Singular Learning Theory addresses this issue through the local learning coefficient (LLC), which characterizes the effective complexity of a model near a given solution. Existing methods for estimating the LLC often rely on posterior sampling, which can be computationally expensive for large neural networks. This makes accurate LLC estimation difficul

Read original source ↗ Open in workspace

recordType

paper

region

Global