Title: Deep Attentive Time Series Modelling for Quantitative Finance Author: Fernando Moreno Pino Supervisors: Antonio Artés Rodríguez, Pablo M. Olmos Abstract: Time series modelling and forecasting is a persistent problem with extensive implications in scientific, business, industrial, and economic areas. This thesis’ contribution is twofold. Firstly, we propose a novel probabilistic time series forecasting methodology that introduces…
Title Regularizing transformers with deep probabilistic layers Authors Aurora Cobo Aguilera, Pablo M. Olmos, Antonio Artés-Rodríguez and Fernando Pérez-Cruz Abstract Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variational Autoencoder…
“Inteligencia Artificial para la medición de PROs en pacientes Oncológicos”, I Edición Farmaimpulso OncoHematologia 2023.
Title: “A Step Towards Advancing Digital Phenotyping In Mental Healthcare” Advisor: Antonio Artés Rodríguez. ABSTRACT Smartphones and wrist-wearable devices have penetrated our lives in recent years. According to published statistics, nearly 84% of the world’s population owns a smartphone, and almost 10% own a wearable device today (2022). These devices continuously generate various data sources…
Title: Change-Point Detection Methods for Behavioral Shift Recognition in Mental Healthcare Author: Lorena Romero (GTS) ABSTRACT Human behavior analysis has been approached from different perspectives along time. In recent years, the emergence of new technologies and digitalization advances have risen as an alternative tool for behavior characterization, as well as for the detection of changes over time. In particular,…
Title: “Objective Assessment of Psychiatric Patients via Machine Learning” Advisor: Antonio Artés Rodríguez. ABSTRACT Mental disorders are still a source of not-well-understood human suffering. They affect one out of four people in the world, and they are more costly to treat than cancer and diabetes together. One of the reasons of the high economic burden…
Title: “Probabilistic Models and Natural Language Processing in Health” Advisor: Antonio Artés Rodríguez. ABSTRACT We are living the Artificial Intelligence (AI) era. Whatever we want, we are surrounded by technology, computers, intelligent devices, and incredible machines able to perform more and more complex activities from our daily routine, from a writing predictor to a face…
Title: “Nested filtering methods for Bayesian inference in state space models” Advisor: Joaquín Míguez Arenas. ABSTRACT A common feature to many problems in some of the most active fields of science is the need to calibrate (i.e., estimate the parameters) and then forecast the time evolution of high-dimensional dynamical systems using sequentially collected data. In…
Title Medical Data Wrangling With Sequential Variational Autoencoders Authors D. Barrejón, Pablo M. Olmos and Antonio Artés-Rodríguez Abstract 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 that these patterns occur in bursts due to sensors…
In this aid, the beneficiary is Alejandro Cano Sánchez and the abstract of the project is the following. The project is focused on the analysis and development of advanced methods for orbital uncertainty characterization applied to space surveillance and tracking of space debris, including the following aspects: modelling of the uncertainty in dynamical models and…
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…
Authors Pablo Moreno-Muñoz, Antonio Artes-Rodríguez, Mauricio Álvarez Abstract 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, each model being characterised by its hyperparameters, pseudo-inputs and their corresponding posterior densities, one could build ensemble GP models without…
Area of research: computational statistics Keywords: Bayesian computation, Monte Carlo, particle filtering, high-dimensional dynamical models, machine learning, aerospace engineering, e-health. Duration: 3+1 years Gross salary: 17,000€ per year Contact: Prof. Joaquin Miguez (joaquin.miguez@uc3m.es) We are opening two positions for PhD students within the Signal Processing & Learning Group. Candidates should have a background in mathematics,…
Area of research: computational statistics Keywords: Bayesian computation, Monte Carlo, particle filtering, high-dimensional dynamical models, machine learning, aerospace engineering, e-health. Duration: 1+1 years Gross salary: 24,000€ per year Contact: Prof. Joaquin Miguez (joaquin.miguez@uc3m.es) We are opening a postdoctoral position within the Signal Processing & Learning Group, to conduct research in problems related to Bayesian estimation,…
September 2020 The article “Actigraphic recording of motor activity in depressed inpatients: A novel computational approach to prediction of clinical course and hospital discharge” by Ignacio Peis, Javier-David Lopez-Morinigo, M. Mercedes Perez-Rodriguez, Maria-Luisa Barrigon, Marta Ruiz-Gomez, Antonio Artés-Rodríguez and Enrique Baca-García has been accepted for publication in Scientific Reports. Title: Actigraphic recording of motor activity in depressed inpatients: A…