26 citations · 65 across the 8 of their papers we have counts for
3 papers · 1 filter
Universal Approximation Theorem for Vector- and Hypercomplex-Valued Neural Networks
Marcos Eduardo Valle, Wington L. Vital, Guilherme Vieira
The universal approximation theorem states that a neural network with one hidden layer can approximate continuous functions on compact sets with any desired precision. This theorem…
Extending the Universal Approximation Theorem for a Broad Class of Hypercomplex-Valued Neural Networks
Wington L. Vital, Guilherme Vieira, Marcos Eduardo Valle
The universal approximation theorem asserts that a single hidden layer neural network approximates continuous functions with any desired precision on compact sets. As an existentia…
A General Framework for Hypercomplex-valued Extreme Learning Machines
Guilherme Vieira, Marcos Eduardo Valle
This paper aims to establish a framework for extreme learning machines (ELMs) on general hypercomplex algebras. Hypercomplex neural networks are machine learning models that featur…