Deep-layered machines have a built-in Occam's razor

Fuente: arXiv
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Main Author: Fink, Thomas M. A.
Format: Preprint
Published: 2026
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author Fink, Thomas M. A.
author_facet Fink, Thomas M. A.
contents Input-output maps are prevalent throughout science and technology. They are empirically observed to be biased towards simple outputs, but we don't understand why. To address this puzzle, we study the archetypal input-output map: a deep-layered machine in which every node is a Boolean function of all the nodes below it. We give an exact theory for the distribution of outputs, and we confirm our predictions through extensive computer experiments. As the network depth increases, the distribution becomes exponentially biased towards simple outputs. This suggests that deep-layered machines and other learning methodologies may be inherently biased towards simplicity in the models that they generate.
format Preprint
id arxiv_https___arxiv_org_abs_2603_01217
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Deep-layered machines have a built-in Occam's razor
Fink, Thomas M. A.
Disordered Systems and Neural Networks
Cellular Automata and Lattice Gases
Input-output maps are prevalent throughout science and technology. They are empirically observed to be biased towards simple outputs, but we don't understand why. To address this puzzle, we study the archetypal input-output map: a deep-layered machine in which every node is a Boolean function of all the nodes below it. We give an exact theory for the distribution of outputs, and we confirm our predictions through extensive computer experiments. As the network depth increases, the distribution becomes exponentially biased towards simple outputs. This suggests that deep-layered machines and other learning methodologies may be inherently biased towards simplicity in the models that they generate.
title Deep-layered machines have a built-in Occam's razor
topic Disordered Systems and Neural Networks
Cellular Automata and Lattice Gases
url https://arxiv.org/abs/2603.01217