1.1k citations · 2.3k across the 40 of their papers we have counts for
15 papers · 1 filter
FEMa-FS: Finite Element Machines for Feature Selection
Lucas Biaggi, João P. Papa, Kelton A. P Costa +2
Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emer…
PL-kNN: A Parameterless Nearest Neighbors Classifier
Danilo Samuel Jodas, Leandro Aparecido Passos, Ahsan Adeel +1
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The -Nearest Neighbors is one of the most effective…
Handling Imbalanced Datasets Through Optimum-Path Forest
Leandro Aparecido Passos, Danilo S. Jodas, Luiz C. F. Ribeiro +3
In the last decade, machine learning-based approaches became capable of performing a wide range of complex tasks sometimes better than humans, demanding a fraction of the time. Suc…
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification
Gustavo Henrique de Rosa, Mateus Roder, João Paulo Papa
Machine Learning has attracted considerable attention throughout the past decade due to its potential to solve far-reaching tasks, such as image classification, object recognition,…
Modeling Implicit Bias with Fuzzy Cognitive Maps
Gonzalo Nápoles, Isel Grau, Leonardo Concepción +2
This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete. In our proposal, problem features are ma…
Speeding Up OPFython with Numba
Gustavo H. de Rosa, João Paulo Papa
A graph-inspired classifier, known as Optimum-Path Forest (OPF), has proven to be a state-of-the-art algorithm comparable to Logistic Regressors, Support Vector Machines in a wide…
Image Denoising using Attention-Residual Convolutional Neural Networks
Rafael G. Pires, Daniel F. S. Santos, Marcos C. S. Santana +2
During the image acquisition process, noise is usually added to the data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the da…
Energy-based Dropout in Restricted Boltzmann Machines: Why not go random
Mateus Roder, Gustavo H. de Rosa, Victor Hugo C. de Albuquerque +2
Deep learning architectures have been widely fostered throughout the last years, being used in a wide range of applications, such as object recognition, image reconstruction, and s…
A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines
Leandro Aparecido Passos, João Paulo Papa
Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable rang…
PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection
Leandro Aparecido Passos, Danilo Samuel Jodas, Luiz C. F. Ribeiro +2
Breast cancer is among the most deadly diseases, distressing mostly women worldwide. Although traditional methods for detection have presented themselves as valid for the task, the…
Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann Machines
Gustavo H. de Rosa, Mateus Roder, João P. Papa
Machine Learning has been applied in a wide range of tasks throughout the last years, ranging from image classification to autonomous driving and natural language processing. Restr…
MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values
Claudio Filipi Goncalves do Santos, Danilo Colombo, Mateus Roder +1
Different techniques have emerged in the deep learning scenario, such as Convolutional Neural Networks, Deep Belief Networks, and Long Short-Term Memory Networks, to cite a few. In…
Learnergy: Energy-based Machine Learners
Mateus Roder, Gustavo Henrique de Rosa, João Paulo Papa
Throughout the last years, machine learning techniques have been broadly encouraged in the context of deep learning architectures. An exciting algorithm denoted as Restricted Boltz…
OPFython: A Python-Inspired Optimum-Path Forest Classifier
Gustavo Henrique de Rosa, João Paulo Papa, Alexandre Xavier Falcão
Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification…
Temperature-Based Deep Boltzmann Machines
Leandro Aparecido Passos Junior, Joao Paulo Papa
Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications, that range from speech recognition to face-base…