A distributed learning algorithm for Self-Organizing Maps intended for outlier analysis in the GAIA - ESA mission. - Daniel Garabato; Carlos Dafonte; Minia Manteiga; Diego Fustes; Marco Antonio Álvarez; Bernardino Arcay Varela (IFSA-EUSFLAT, 2015)

Analysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releases. - Marco Antonio Álvarez; Carlos Dafonte; Daniel Garabato; Minia Manteiga (ICONIP (4), 2016)

On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra. - Carlos Dafonte; Diego Fustes; Minia Manteiga; Daniel Garabato; Marco Antonio Álvarez; Ana Ulla; C. Allende Prieto (CoRR, 2016)

Distributed Unsupervised Clustering for Outlier Analysis in the Biggest Milky Way Survey: ESA Gaia Mission. - Daniel Garabato; Carlos Dafonte; Marco Antonio Álvarez; Minia Manteiga (UCAmI, 2017)

Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis. - Carlos Dafonte; Daniel Garabato; Marco Antonio Álvarez; Minia Manteiga (Sensors, 2018)

Network Data Flow Clustering based on Unsupervised Learning. - Manuel F. López-Vizcaíno; Carlos Dafonte; Francisco Javier Nóvoa; Daniel Garabato; Marco Antonio Álvarez; Diego Fernández 001 (NCA, 2019)

GUASOM: an adaptive visualization tool for unsupervised clustering in spectrophotometric astronomical surveys. - Marco Antonio Álvarez; Carlos Dafonte; Minia Manteiga; Daniel Garabato; Raúl Santoveña (Neural Comput. Appl., 2022)

Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks. - Minia Manteiga; Raúl Santoveña; Marco Antonio Álvarez; Carlos Dafonte; Manuel G. Penedo; Silvana G. Navarro; Luis J. Corral (CoRR, 2025)