JMLR
Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data
Authors
Thomas Chen
Patrícia Muñoz Ewald
Research Topics
Machine Learning
Paper Information
-
Journal:
Journal of Machine Learning Research -
Added to Tracker:
Sep 08, 2025
Abstract
We explicitly construct zero loss neural network classifiers. We write the weight matrices and bias vectors in terms of cumulative parameters, which determine truncation maps acting recursively on input space. The configurations for the training data considered are $(i)$ sufficiently small, well separated clusters corresponding to each class, and $(ii)$ equivalence classes which are sequentially linearly separable. In the best case, for $Q$ classes of data in $\mathbb{R}^{M}$, global minimizers can be described with $Q(M+2)$ parameters.
Author Details
Thomas Chen
AuthorPatrícia Muñoz Ewald
AuthorResearch Topics & Keywords
Machine Learning
Research AreaCitation Information
APA Format
Thomas Chen
&
Patrícia Muñoz Ewald
.
Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data.
Journal of Machine Learning Research
.
BibTeX Format
@article{paper482,
title = { Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data },
author = {
Thomas Chen
and Patrícia Muñoz Ewald
},
journal = { Journal of Machine Learning Research },
url = { https://www.jmlr.org/papers/v26/24-1516.html }
}