Constraining the outputs of ReLU neural networks

Fuente: arXiv
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Main Authors: Alexandr, Yulia, Montúfar, Guido
Format: Preprint
Published: 2025
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author Alexandr, Yulia
Montúfar, Guido
author_facet Alexandr, Yulia
Montúfar, Guido
contents We introduce a class of algebraic varieties naturally associated with ReLU neural networks, arising from the piecewise linear structure of their outputs across activation regions in input space, and the piecewise multilinear structure in parameter space. By analyzing the rank constraints on the network outputs within each activation region, we derive polynomial equations that characterize the functions representable by the network. We further investigate conditions under which these varieties attain their expected dimension, providing insight into the expressive and structural properties of ReLU networks.
format Preprint
id arxiv_https___arxiv_org_abs_2508_03867
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Constraining the outputs of ReLU neural networks
Alexandr, Yulia
Montúfar, Guido
Algebraic Geometry
Machine Learning
We introduce a class of algebraic varieties naturally associated with ReLU neural networks, arising from the piecewise linear structure of their outputs across activation regions in input space, and the piecewise multilinear structure in parameter space. By analyzing the rank constraints on the network outputs within each activation region, we derive polynomial equations that characterize the functions representable by the network. We further investigate conditions under which these varieties attain their expected dimension, providing insight into the expressive and structural properties of ReLU networks.
title Constraining the outputs of ReLU neural networks
topic Algebraic Geometry
Machine Learning
url https://arxiv.org/abs/2508.03867