104 citations · 129 across the 9 of their papers we have counts for
6 papers · 1 filter
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks
Claudio Filipi Goncalves do Santos, Mateus Roder, Leandro A. Passos +1
In the last decade, exponential data growth supplied the machine learning-based algorithms' capacity and enabled their usage in daily life activities. Additionally, such an improve…
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,…
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…
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…