89 citations · 167 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça +3
Machine Learning (ML) algorithms have been increasingly applied to problems from several different areas. Despite their growing popularity, their predictive performance is usually…
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça +2
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come…
Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms
Rafael Gomes Mantovani, Tomáš Horváth, André L. D. Rossi +4
Machine learning algorithms often contain many hyperparameters (HPs) whose values affect the predictive performance of the induced models in intricate ways. Due to the high number…