Marcos Eduardo Valle | DeepAI

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Rodolfo Anibal Lobo
4 publications
Guilherme Vieira
4 publications
Angelica Lourenço Oliveira
3 publications
Fidelis Zanetti de Castro
2 publications
Alex Santana dos Santos
1 publication
Marco Aurélio Granero
1 publication
Cristhian Xavier Hernández
1 publication
Samuel Francisco
1 publication
Wington L. Vital
1 publication

∙ 09/14/2023

Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks

Despite the many successful applications of deep learning models for mul...

0 Marcos Eduardo Valle, et al. ∙

∙ 06/30/2023

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

Mathematical morphology is a theory concerned with non-linear operators ...

0 Samuel Francisco, et al. ∙

∙ 09/06/2022

Extending the Universal Approximation Theorem for a Broad Class of Hypercomplex-Valued Neural Networks

The universal approximation theorem asserts that a single hidden layer n...

0 Wington L. Vital, et al. ∙

∙ 05/26/2022

Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks

This paper features convolutional neural networks defined on hypercomple...

0 Guilherme Vieira, et al. ∙

∙ 12/13/2021

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

The field of neural networks has seen significant advances in recent yea...

1 Marco Aurélio Granero, et al. ∙

∙ 12/13/2021

On the Dynamics of Hopfield Neural Networks on Unit Quaternions

In this paper, we first address the dynamics of the elegant multi-valued...

0 Marcos Eduardo Valle, et al. ∙

∙ 07/12/2021

Least-Squares Linear Dilation-Erosion Regressor Trained using Stochastic Descent Gradient or the Difference of Convex Methods

This paper presents a hybrid morphological neural network for regression...

0 Angelica Lourenço Oliveira, et al. ∙

∙ 01/15/2021

A General Framework for Hypercomplex-valued Extreme Learning Machines

This paper aims to establish a framework for extreme learning machines (...

7 Guilherme Vieira, et al. ∙

∙ 11/11/2020

Linear Dilation-Erosion Perceptron Trained Using a Convex-Concave Procedure

Mathematical morphology (MM) is a theory of non-linear operators used fo...

0 Angelica Lourenço Oliveira, et al. ∙

∙ 11/11/2020

Linear Dilation-Erosion Perceptron for Binary Classification

In this work, we briefly revise the reduced dilation-erosion perceptron ...

0 Angelica Lourenço Oliveira, et al. ∙

∙ 09/18/2020

Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories

An ensemble method should cleverly combine a group of base classifiers t...

0 Rodolfo Anibal Lobo, et al. ∙

∙ 03/04/2020

Reduced Dilation-Erosion Perceptron for Binary Classification

Dilation and erosion are two elementary operations from mathematical mor...

0 Marcos Eduardo Valle, et al. ∙

∙ 01/31/2020

Hypercomplex-Valued Recurrent Correlation Neural Networks

Recurrent correlation neural networks (RCNNs), introduced by Chiueh and ...

0 Marcos Eduardo Valle, et al. ∙

∙ 01/30/2020

Quaternion-Valued Recurrent Projection Neural Networks on Unit Quaternions

Hypercomplex-valued neural networks, including quaternion-valued neural ...

0 Marcos Eduardo Valle, et al. ∙

∙ 09/19/2019

An Introduction to Quaternion-Valued Recurrent Projection Neural Networks

Hypercomplex-valued neural networks, including quaternion-valued neural ...

0 Marcos Eduardo Valle, et al. ∙

∙ 02/14/2019

A Broad Class of Discrete-Time Hypercomplex-Valued Hopfield Neural Networks

In this paper, we address the stability of a broad class of discrete-tim...

0 Fidelis Zanetti de Castro, et al. ∙

∙ 02/11/2019

Max-C and Min-D Projection Autoassociative Fuzzy Morphological Memories: Theory and Applications for Face Recognition

Max-C and min-D projection autoassociative fuzzy morphological memories ...

0 Alex Santana dos Santos, et al. ∙

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