70 citations · 128 across the 5 of their papers we have counts for
5 papers · 1 filter
Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges
Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez +5
Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyper-parame…
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images
S. Tabik, A. Gómez-Ríos, J. L. Martín-Rodríguez +10
Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT…
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations
Manuel Rey-Area, Emilio Guirado, Siham Tabik +1
It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on. This iss…
A data relocation approach for terrain surface analysis on multi-GPU systems: a case study on the total viewshed problem
A. J. Sanchez-Fernandez, L. F. Romero, G. Bandera +1
Digital Elevation Models (DEMs) are important datasets for modelling the line of sight, such as radio signals, sound waves and human vision. These are commonly analyzed using rotat…
MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal
S. Tabik, R. F. Alvear-Sandoval, M. M. Ruiz +3
Ensemble methods have been widely used for improving the results of the best single classificationmodel. A large body of works have achieved better performance mainly by applying o…