8 citations · 15 across the 4 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
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…
cs.LG2023★ 7 cited
Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks
Marcos Eduardo Valle
Despite the many successful applications of deep learning models for multidimensional signal and image processing, most traditional neural networks process data represented by (mul…