Publications

Displaying 76 - 100 of 1307

Publication	Date
Accurate long-term air temperature prediction with Machine Learning models and data reduction techniques Authors: D. Fister,J. Pérez-Aracil,C. Peláez-Rodríguez,J. Del Ser,S. Salcedo-Sanz In this paper, three customised Artificial Intelligence (AI) frameworks, considering Deep Learning, Machine Learning (ML) algorithms and data reduction techniques, are proposed for a problem of long-term summer air temperature prediction. Specifically, the prediction of the average air temperature… Elsevier BV	2023-03-01
Scientific modelling can be accessible, interoperable and user friendly: A case study for pasture and livestock modelling in Spain Authors: Alba Marquez Torres,Stefano Balbi,Ferdinando Villa This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to… Public Library of Science (PLoS)	2023-02-24
Clinical severity prediction of COVID-19 admitted patients in Spain: SEMI and REDISSEC cohorts Authors: Mario Martínez-García,Susana García-Gutierrez,Lasai Barreñada Taleb,Rubén Armañanzas,Inaki Inza,Jose A. Lozano AbstractThis report addresses, from a machine learning perspective, a multi-class classification problem to predict the first deterioration level of a COVID-19 positive patient at the time of hospital admission. Socio-demographic features, laboratory tests and other measures are taken into account… Cold Spring Harbor Laboratory	2023-02-10
Image captioning for effective use of language models in knowledge-based visual question answering Authors: Ander Salaberria,Gorka Azkune,Oier Lopez de Lacalle,Aitor Soroa,Eneko Agirre Under review. 25 pages with 4 figures Elsevier BV	2023-02-01
A Deep Fourier Residual method for solving PDEs using Neural Networks Authors: Jamie M. Taylor,David Pardo,Ignacio Muga When using Neural Networks as trial functions to numerically solve PDEs, a key choice to be made is the loss function to be minimised, which should ideally correspond to a norm of the error. In multiple problems, this error norm coincides with--or is equivalent to--the $H^{-1}$-norm of the residual… Elsevier BV	2023-02-01
A Deep Double Ritz Method (D2RM) for solving Partial Differential Equations using Neural Networks Authors: Carlos Uriarte,David Pardo,Ignacio Muga,Judit Muñoz-Matute Elsevier BV	2023-02-01
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels Authors: Ming Li,Yingying Fang,Zeyu Tang,Chibudom Onuorah,Jun Xia,Javier Del Ser,Simon Walsh,Guang Yang The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT)… Institute of Electrical and Electronics Engineers (IEEE)	2023-02-01
Memory-Based Monte Carlo Integration for Solving Partial Differential Equations Using Neural Networks Authors: Carlos Uriarte,Jamie M. Taylor,David Pardo,Oscar A. Rodríguez,Patrick Vega Monte Carlo integration is a widely used quadrature rule to solve Partial Differential Equations with neural networks due to its ability to guarantee overfitting-free solutions and high-dimensional scalability. However, this stochastic method produces noisy losses and gradients during training,… Springer Nature Switzerland	2023-01-01
Learning the progression patterns of treatments using a probabilistic generative model Authors: Onintze Zaballa,Aritz Pérez,Elisa Gómez Inhiesto,Teresa Acaiturri Ayesta,Jose A. Lozano Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model of treatments that are described in terms of sequences of medical activities… Elsevier BV	2023-01-01
Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations Authors: Ana Fernandez-Navamuel,David Pardo,Filipe Magalhães,Diego Zamora-Sánchez,Ángel J. Omella,David Garcia-Sanchez This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to… Elsevier BV	2023-01-01
Variable selection with LASSO regression for complex survey data Authors: Amaia Iparragirre,Thomas Lumley,Irantzu Barrio,Inmaculada Arostegui Variable selection is an important step to end up with good prediction models. LASSO regression models are one of the most commonly used methods for this purpose, for which cross‐validation is the most widely applied validation technique to choose the tuning parameter . Validation techniques in a… Wiley	2023-01-01
Unsupervised Domain Adaption for Neural Information Retrieval Authors: Dominguez, Carlos,Campos, Jon Ander,Agirre, Eneko,Azkune, Gorka Neural information retrieval requires costly annotated data for each target domain to be competitive. Synthetic annotation by query generation using Large Language Models or rule-based string manipulation has been proposed as an alternative, but their relative merits have not been analysed. In this… Elsevier BV	2023-01-01
State-of-the-Art in Language Technology and Language-centric Artificial Intelligence Authors: Rodrigo Agerri,Eneko Agirre,Itziar Aldabe,Nora Aranberri,Jose Maria Arriola,Aitziber Atutxa,Gorka Azkune,Jon Ander Campos,Arantza Casillas,Ainara Estarrona,Aritz Farwell,Iakes Goenaga,Josu Goikoetxea,Koldo Gojenola,Inma Hernáez,Mikel Iruskieta,Gorka… AbstractThis chapter landscapes the field of Language Technology (LT) and language- centric AI by assembling a comprehensive state-of-the-art of basic and applied research in the area. It sketches all recent advances in AI, including the most recent deep learning neural technologies. The chapter… Springer International Publishing	2023-01-01
Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm Authors: Jesús Para,Javier Del Ser,Antonio J. Nebro Springer Nature Switzerland	2023-01-01
Bridge Damage Identification Under Varying Environmental and Operational Conditions Combining Deep Learning and Numerical Simulations Authors: Ana Fernandez-Navamuel,David Pardo,Filipe Magalhães,Diego Zamora-Sánchez,Ángel J. Omella,David Garcia-Sanchez This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to… Elsevier BV	2023-01-01
Automatic Logical Forms improve fidelity in Table-to-Text generation Authors: Iñigo Alonso,Eneko Agirre Table-to-text systems generate natural language statements from structured data like tables. While end-to-end techniques suffer from low factual correctness (fidelity), a previous study reported gains when using manual logical forms (LF) that represent the selected content and the semantics of the… Elsevier BV	2023-01-01
Estimation of the ROC curve and the area under it with complex survey data Authors: Amaia Iparragirre,Irantzu Barrio,Inmaculada Arostegui Logistic regression models are widely applied in daily practice. Hence, it is necessary to ensure they have an adequate predictive performance, which is usually estimated by means of the receiver operating characteristic (ROC) curve and the area under it (area under the curve [AUC]). Traditional… Wiley	2023-01-01
Automatic Logical Forms Improve Fidelity in Table-to-Text Generation Authors: Iñigo Alonso,Eneko Agirre Table-to-text systems generate natural language statements from structured data like tables. While end-to-end techniques suffer from low factual correctness (fidelity), a previous study reported gains when using manual logical forms (LF) that represent the selected content and the semantics of the… Elsevier BV	2023-01-01
Hyper-parameter Optimization Using Continuation Algorithms Authors: Jairo Rojas-Delgado,J. A. Jiménez,Rafael Bello,J. A. Lozano Springer International Publishing	2023-01-01
Estimating Future Costs of Emerging Wave Energy Technologies Authors: Pablo Ruiz-Minguela,Donald R. Noble,Vincenzo Nava,Shona Pennock,Jesus M. Blanco,Henry Jeffrey The development of new renewable energy technologies is generally perceived as a critical factor in the fight against climate change. However, significant difficulties arise when estimating the future performance and costs of nascent technologies such as wave energy. Robust methods to estimate the… MDPI AG	2022-12-23
Evolutionary Multitask Optimization: Fundamental research questions, practices, and directions for the future Authors: Eneko Osaba,Javier Del Ser,Ponnuthurai N. Suganthan 17 pages, 3 figures. This papers has been accepted and published in Swarm and Evolutionary Computation under this DOI: 10.1016/j.swevo.2022.101203 Elsevier BV	2022-12-01
Greybox XAI: A Neural-Symbolic learning framework to produce interpretable predictions for image classification Authors: Adrien Bennetot,Gianni Franchi,Javier Del Ser,Raja Chatila,Natalia Díaz-Rodríguez Accepted in Knowledge-Based Systems Journal Elsevier BV	2022-12-01
Bayesian Performance Analysis for Algorithm Ranking Comparison Authors: Jairo Rojas-Delgado,Josu Ceberio,Borja Calvo,Jose A. Lozano Institute of Electrical and Electronics Engineers (IEEE)	2022-12-01
Eigenvalue Curves for Generalized MIT Bag Models Authors: Naiara Arrizabalaga,Albert Mas,Tomás Sanz-Perela,Luis Vega We study spectral properties of Dirac operators on bounded domains $Ω\subset \mathbb{R}^3$ with boundary conditions of electrostatic and Lorentz scalar type and which depend on a parameter $τ\in\mathbb{R}$; the case $τ= 0$ corresponds to the MIT bag model. We show that the eigenvalues are… Springer Science and Business Media LLC	2022-11-22
A painless multi-level automatic goal-oriented hp-adaptive coarsening strategy for elliptic and non-elliptic problems Authors: Felipe V. Caro,Vincent Darrigrand,Julen Alvarez-Aramberri,Elisabete Alberdi,David Pardo Elsevier BV	2022-11-01