An elementary proof of a universal approximation theorem

Chris Monico
6/14/2024
cs.LG

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

In this short note, we give an elementary proof of a universal approximation theorem for neural networks with three hidden layers and increasing, continuous, bounded activation function. The result is weaker than the best known results, but the proof is elementary in the sense that no machinery beyond undergraduate analysis is used.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

A Survey on Universal Approximation Theorems

90% match2024

On the Expressive Power of Neural Networks

87% match2023

Universal Approximation Theorem for Input-Connected Multilayer Perceptrons

86% match2026

Geometric separation and constructive universal approximation with two hidden layers

86% match2026

Universal approximation theorem for neural networks with inputs from a topological vector space

85% match2024

Approximation properties of neural ODEs

85% match2025

Cite this paper

@article{monico2024elementary,
  title  = {An elementary proof of a universal approximation theorem},
  author = {Chris Monico},
  year   = {2024},
  eprint = {2406.10002},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.10002v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2406.10002.svg)](https://codeofpaper.com/paper/2406.10002)

Discussion