Approximation error of single hidden layer neural networks with fixed weights

TL;DR

This paper derives an explicit formula to quantify the approximation error of single hidden layer neural networks with two fixed weights, enhancing understanding of their capabilities.

Contribution

It introduces a precise formula for the approximation error of neural networks with fixed weights, a novel analytical result in the field.

Findings

Explicit formula for approximation error derived

Improved understanding of fixed-weight neural network capabilities

Potential implications for neural network design and analysis

Abstract

This paper provides an explicit formula for the approximation error of single hidden layer neural networks with two fixed weights.