You can find most of my articles on my Google Scholar profile.
Axel Brando, Jose A. Rodrıguez-Serrano, Mauricio Ciprian, Roberto Maestre and Jordi Vitrià, Uncertainty Modelling in Deep Networks for Short and Noisy Time Series Forecasting. ECMLPKDD 2018.
Jordi Vitrià. Let’s Open the Black Box of Deep Learning! In Business Intelligence and Big Data, Edited by Zimanyi, Esteban. Springer International Publishing. 2018.
Guillem Pascual, Santi Seguí, Jordi Vitria. Uncertainty Gated Network for Land Cover Segmentation. Poster at DeepGlobe CVPR 2018 workshop.
We (E.Puertas, S.Seguí, O.Pujol and J.Vitrià) have written a book entitled "El Poder de los datos : del big data al aprendizaje profundo" for RBA Editores & National Geographic. The book has already been translated to English and Italian!
We have written a book entitled Introduction to Data Science. A Python Approach to Concepts, Techniques and Applications for Springer. The book, with a large set of Jupyter notebooks , is the companion of a hands-on course on Data Science.
We have presented the result of a collaboration with BBVA D&A in the Workshop on Machine Learning for Spatiotemporal Forecasting as part of the NIPS 2016 conference. Our contribution is entitled "Evaluating uncertainty scores for deep regression networks in financial short time series forecasting".
Seguí, Santi, Michal Drozdzal, Guillem Pascual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz and Jordi Vitrià. “Generic feature learning for wireless capsule endoscopy analysis.” Computers in biology and medicine 79 (2016): 163-172.