48 citations · 92 across the 15 of their papers we have counts for
18 papers
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data
María Martínez-García, Fernando Moreno-Pino, Pablo M. Olmos +1
Sleep constitutes a key indicator of human health, performance, and quality of life. Sleep deprivation has long been related to the onset, development, and worsening of several men…
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data
Fernando Moreno-Pino, María Martínez-García, Pablo M. Olmos +1
Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over t…
PyHHMM: A Python Library for Heterogeneous Hidden Markov Models
Fernando Moreno-Pino, Emese Sükei, Pablo M. Olmos +1
We introduce PyHHMM, an object-oriented open-source Python implementation of Heterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic core functionalities, such as di…
Modular Gaussian Processes for Transfer Learning
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a framework for transfer learning based on modular variational Gaussian processes (GP). We develop a module-based method that having a dictionary of well fitted GPs, one…
Regularizing Transformers With Deep Probabilistic Layers
Aurora Cobo Aguilera, Pablo Martínez Olmos, Antonio Artés-Rodríguez +1
Language models (LM) have grown with non-stop in the last decade, from sequence-to-sequence architectures to the state-of-the-art and utter attention-based Transformers. In this wo…
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…
Medical data wrangling with sequential variational autoencoders
Daniel Barrejón, Pablo M. Olmos, Antonio Artés-Rodríguez
Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is tha…
Unsupervised Learning of Global Factors in Deep Generative Models
Ignacio Peis, Pablo M. Olmos, Antonio Artés-Rodríguez
We present a novel deep generative model based on non i.i.d. variational autoencoders that captures global dependencies among observations in a fully unsupervised fashion. In contr…
Recyclable Gaussian Processes
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a new framework for recycling independent variational approximations to Gaussian processes. The main contribution is the construction of variational ensembles given a di…
Multinomial Sampling for Hierarchical Change-Point Detection
Lorena Romero-Medrano, Pablo Moreno-Muñoz, Antonio Artés-Rodríguez
Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series. We assume that change-points lie on a lower…
Robust Sampling in Deep Learning
Aurora Cobo Aguilera, Antonio Artés-Rodríguez, Fernando Pérez-Cruz +1
Deep learning requires regularization mechanisms to reduce overfitting and improve generalization. We address this problem by a new regularization method based on distributional ro…
Deep Sequential Models for Suicidal Ideation from Multiple Source Data
Ignacio Peis, Pablo M. Olmos, Constanza Vera-Varela +4
This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential mode…
Continual Learning for Infinite Hierarchical Change-Point Detection
Pablo Moreno-Muñoz, David Ramírez, Antonio Artés-Rodríguez
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations. When Bayesian methods are considered, the standard practice is…
Change-Point Detection on Hierarchical Circadian Models
Pablo Moreno-Muñoz, David Ramírez, Antonio Artés-Rodríguez
This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to…
Heterogeneous Multi-output Gaussian Process Prediction
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs. We assume that each output has its own likelihood function and use a vector-valu…
Individual performance calibration using physiological stress signals
Francisco Hernando-Gallego, Antonio Artés-Rodríguez
The relation between performance and stress is described by the Yerkes-Dodson Law but varies significantly between individuals. This paper describes a method for determining the in…
Bayesian Nonparametric Crowdsourcing
Pablo G. Moreno, Yee Whye Teh, Fernando Perez-Cruz +1
Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce r…
Blind Analysis of EGM Signals: Sparsity-Aware Formulation
David Luengo, Javier Via, Sandra Monzon +2
This technical note considers the problems of blind sparse learning and inference of electrogram (EGM) signals under atrial fibrillation (AF) conditions. First of all we introduce…