Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis

Understanding and controlling the complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and model capacity. While most approaches rely on entropy-based loss functions and statistical metrics, these measures often fail to capture...

Fuld beskrivelse

Bibliografiske detaljer

Principais autores:	Eduardo Y. Sakabe, Felipe S. Abrahão, Alexandre Simões, Esther Colombini, Paula Costa, Ricardo Gudwin, Hector Zenil
Format:	Artigo
Sprog:	Inglês
Udgivet:	Frontiers Media S.A. 2026-05-01
Serier:	Frontiers in Computational Neuroscience
Fag:	algorithmic information theory binarized neural networks Block Decomposition Method learning as compression Shannon entropy
Online adgang:	https://www.frontiersin.org/articles/10.3389/fncom.2026.1791546/full
Tags:	Tilføj Tag Ingen Tags, Vær først til at tagge denne postø!