activity
20192023
most citedOn the Dynamics of Hopfield Neural Networks on Unit Quaternions

51 citations · 191 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CV2023★ 1 cited

Shortest Length Total Orders Do Not Minimize Irregularity in Vector-Valued Mathematical Morphology

Samuel Francisco, Marcos Eduardo Valle

Mathematical morphology is a theory concerned with non-linear operators for image processing and analysis. The underlying framework for mathematical morphology is a partially order…

cs.LG2022★ 9 cited

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…

cs.CV2021★ 10 cited

Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis

Marco Aurélio Granero, Cristhian Xavier Hernández, Marcos Eduardo Valle

The field of neural networks has seen significant advances in recent years with the development of deep and convolutional neural networks. Although many of the current works addres…

cs.NE2021★ 51 cited

On the Dynamics of Hopfield Neural Networks on Unit Quaternions

Marcos Eduardo Valle, Fidelis Zanetti de Castro

In this paper, we first address the dynamics of the elegant multi-valued quaternionic Hopfield neural network (MV-QHNN) proposed by Minemoto and collaborators. Contrary to what was…

cs.LG2021

Least-Squares Linear Dilation-Erosion Regressor Trained using a Convex-Concave Procedure

Angelica Lourenço Oliveira, Marcos Eduardo Valle

This paper presents a hybrid morphological neural network for regression tasks called linear dilation-erosion regressor (-DER). An -DER is given by a convex combination…

cs.LG2021★ 26 cited

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…