Title: Advanced Inference and Representation Learning Methods in Variational Autoencoders. Author: Ignacio Peis Aznarte Abstract: Deep Generative Models have gained significant popularity in the Machine Learning research community since the early 2010s. These models allow to generate realistic data by leveraging the power of Deep Neural Networks. The field experienced a signficant breakthrough when Variational Autoencoders (VAEs)…
Title: How can humans leverage machine learning? From Medical Data Wrangling to Learning to Defer to Multiple Experts. Author: Daniel Barrejon Moreno. Abstract:The irruption of the smartphone into everyone’s life and the ease with which we digitise or record any data supposed an explosion of quantities of data. Smartphones, equipped with advanced cameras and sensors, have empowered…
Title Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation Authors Emese Sukei, Santiago de Leon Martinez, Pablo M. Olmos and Antonio Artés-Rodríguez Abstract Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or…
Title One-week suicide risk prediction using real-time smartphone monitoring Authors Maria Luisa Barrigon; Lorena Romero-Medrano; Pablo Moreno-Muñoz; Alejandro Porras-Segovia; Jorge Lopez-Castroman; Philippe Courtet; Antonio Artés-Rodríguez; Enrique Baca-Garcia Abstract Background: Suicide is a major global public health issue becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological…
Antonio Artés Rodríguez and Pablo Martínez Olmos have recieved funding for two collaborative research projects within the call «Medicina Personalizada de Precisión» granted by Instituto de Salud Carlos III de Madrid. Antonio Artés Rodríguez Title: Integrating longitudinal patient-generated data and multi-omic profiling for comprehensive precision oncology in womens’ cancers Ref.: PMP22/00032 Principal Investigator: Quintela, Miguel…
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.
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…