84 citations · 394 across the 35 of their papers we have counts for
7 papers · 1 filter
Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering
Óscar Jiménez Rama, Fernando Moreno-Pino, David Ramírez +1
The best encoding is the one that is interpretable in nature. In this work, we introduce a novel model that incorporates an interpretable bottleneck-termed the Filter Bank (FB)-at…
Multi-task longitudinal forecasting with missing values on Alzheimer's Disease
Carlos Sevilla-Salcedo, Vandad Imani, Pablo M. Olmos +2
Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missin…
Deep Autoregressive Models with Spectral Attention
Fernando Moreno-Pino, Pablo M. Olmos, Antonio Artés-Rodríguez
Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architectur…
Bayesian Sparse Factor Analysis with Kernelized Observations
Carlos Sevilla-Salcedo, Alejandro Guerrero-López, Pablo M. Olmos +1
Multi-view problems can be faced with latent variable models since they are able to find low-dimensional projections that fairly capture the correlations among the multiple views t…
Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis
Carlos Sevilla-Salcedo, Vanessa Gómez-Verdejo, Pablo M. Olmos
The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate s…
Improved BiGAN training with marginal likelihood equalization
Pablo Sánchez-Martín, Pablo M. Olmos, Fernando Perez-Cruz
We propose a novel training procedure for improving the performance of generative adversarial networks (GANs), especially to bidirectional GANs. First, we enforce that the empirica…