In this aid, the beneficiary is Arturo Armario Romero and the abstract of the project is the following.
The goal of this project, named as ESPECTRO, is to provide eB2 with cutting-edge tools to combine and learn from multi-modal information. On the one hand, by providing classical methods with larger expressivity using BNP/ARD priors combined with NVI. On the other hand, by limiting the excessive representation ability of deep probabilistic models using automatic relevance determination priors. Handling heterogeneous data types, including time series, is a must in both cases. This machinery will be tested within an on-going research project of eB2 that aims at finding medically-interpretable models able to predict the emotional state and depression risk of psychiatric patients under current treatment.