Does diversity improve deep learning? - R. F. Alvear-Sandoval; Aníbal R. Figueiras-Vidal (EUSIPCO, 2015)

An Experiment in Pre-Emphasizing Diversified Deep Neural Classifiers. - Ricardo F. Alvear-Sandoval; Aníbal R. Figueiras-Vidal (ESANN, 2016)

Experiments in combining boosting and deep stacked networks. - Manuel Montoya-Catala; Ricardo F. Alvear-Sandoval; Aníbal R. Figueiras-Vidal (MLSP, 2016)

On building ensembles of stacked denoising auto-encoding classifiers and their further improvement. - Ricardo F. Alvear-Sandoval; Aníbal R. Figueiras-Vidal (Inf. Fusion, 2018)

On building ensembles of stacked denoising auto-encoding classifiers and their further improvement - a correction. - Ricardo F. Alvear-Sandoval; Aníbal R. Figueiras-Vidal (Inf. Fusion, 2018)

On improving CNNs performance: The case of MNIST. - Ricardo F. Alvear-Sandoval; José-Luis Sancho-Gómez; Aníbal R. Figueiras-Vidal (Inf. Fusion, 2019)

A tutorial on ensembles and deep learning fusion with MNIST as guiding thread: A complex heterogeneous fusion scheme reaching 10 digits error. - Siham Tabik; Ricardo F. Alvear-Sandoval; María M. Ruiz; José-Luis Sancho-Gómez; Aníbal R. Figueiras-Vidal; Francisco Herrera (CoRR, 2020)

MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1% error rate. Ensembles overview and proposal. - Siham Tabik; Ricardo F. Alvear-Sandoval; María M. Ruiz; José-Luis Sancho-Gómez; Aníbal R. Figueiras-Vidal; Francisco Herrera (Inf. Fusion, 2020)