About the connectivity of Fatou components for some families of rational maps 

    Paraschiv, Dan Alexandru (Fecha de defensa: 2023-06-16)

    [eng] Rational iteration is the study of the asymptotic behaviour of the sequences given by the iterates of a rational map on the Riemann sphere. According to Montel's theory on normal families, the phase space (also called ...

    Adapting by copying. Towards a sustainable machine learning 

    Unceta, Irene (Fecha de defensa: 2021-03-01)

    Despite the rapid growth of machine learning in the past decades, deploying automated decision making systems in practice remains a challenge for most companies. On an average day, data scientists face substantial barriers ...

    Advancing 3D Point Cloud Understanding in Real-World Applications 

    Zoumpekas, Athanasios (Fecha de defensa: 2024-06-03)

    [eng] In recent years, rapid advancements in 3D sensing technologies, such as LiDAR, have revolutionized various fields, spanning robotics, augmented reality, earth vision, and industrial manufacturing. Point clouds, ...

    Aleatoric Uncertainty Modelling in Regression Problems using Deep Learning 

    Brando Guillaumes, Axel (Fecha de defensa: 2022-07-18)

    Nowadays, we live in an intrinsically uncertain world from our perspective. We do not know what will happen in the future but, to infer it, we build the so-called models. These models are abstractions of the world we live ...

    Application of Radiomics-based Machine Learning models on complex cardíac diseases 

    Izquierdo, Cristian (Fecha de defensa: 2024-07-18)

    [eng] This doctoral thesis investigates the combination of radiomics and machine learning (ML) within the field of cardiology, emphasizing early detection and prognosis of complex cardiovascular diseases (CVDs). Radiomics ...

    Approximate option pricing for jump-diffusion stochastic volatility models 

    Makumbe, Zororo Stanelake (Fecha de defensa: 2025-01-21)

    [eng] The following is a summary of the above-mentioned thesis. The thesis covers alternative stochastic models, risk management, and option price decomposition. 1.1 Alternative Stochastic Models Several alternative ...

    Automatic Cardiac Segmentation of Complex Morphologies, Modalities and Tissues 

    Martín Isla, Carlos (Fecha de defensa: 2024-01-18)

    [eng] Cardiovascular diseases (CVDs) continue to take a significant toll on global health, highlighting the need for more accurate and efficient diagnostic tools. This thesis, titled "Automatic Cardiac Segmentation of ...

    Blow-up algebras in Algebra, Geometry and Combinatorics 

    Cid Ruiz, Yairon (Fecha de defensa: 2019-06-26)

    The primary topic of this thesis lies at the crossroads of Commutative Algebra and its interactions with Algebraic Geometry and Combinatorics. It is mainly focused around the following themes: I Defining equations ...

    Carotid Artery Ultrasound Image-Based Cardiovascular Risk Prediction using Deep Learning 

    Vila Muñoz, Maria del Mar (Fecha de defensa: 2024-05-03)

    [eng] Cardiovascular Diseases (CVDs), the leading cause of death in developed countries, often involve atherosclerosis, which is a chronic inflammatory thickening of the inner artery layer. Monitoring atherosclerotic plaque ...

    Characterization and Mitigation of Algorithmic Bias in Recommender Systems 

    Gómez Yepes, Elizabeth (Fecha de defensa: 2024-12-17)

    [eng] Recommender Systems are critical in helping users navigate large amounts of information by providing personalized suggestions. However, these systems can exhibit biases, especially when data imbalances exist, leading ...

    Chern degree functions and Prym semicanonical pencils 

    Rojas, Andrés (Fecha de defensa: 2021-11-26)

    Abelian varieties are projective algebraic varieties endowed with a group structure. They constitute one of the most explored objects in Algebraic Geometry throughout the last decades. On the one hand, abelian varieties ...

    Congested Optimal Transport in the Heisenberg Group 

    Circelli, Michele (Fecha de defensa: 2024-07-03)

    [eng] In this thesis we adapted the problem of continuous congested optimal transport to the Heisenberg group, equipped with a sub-Riemannian metric: we restricted the set of admissible paths to the horizontal curves. We ...

    Contributions to stochastic analysis 

    Binotto, Giulia (Fecha de defensa: 2018-04-13)

    The aim of this dissertation is to present some new results on stochastic analysis. It consists on three works that deal with two Gaussian processes: the Brownian motion and the fractional Brownian motion with Hurst parameter ...

    Contributions to the theory of Large Cardinals through the method of Forcing 

    Poveda Ruzafa, Alejandro (Fecha de defensa: 2020-11-09)

    The present dissertation is a contribution to the field of Mathematical Logic and, more particularly, to the subfield of Set Theory. Within Set theory, we are mainly concerned with the interactions between the largecardinal ...

    Deep Learning and Uncertainty Modeling in Visual Food Analysis 

    Aguilar, Eduardo (Fecha de defensa: 2020-09-23)

    Several computer vision approaches have been proposed for tackling food analysis problems, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. ...

    Deep Multimodal Learning for Egocentric Storytelling and Food Analysis 

    Bolaños Solà, Marc (Fecha de defensa: 2021-04-09)

    The world of Machine Learning and Computer Vision has experienced a revolution since the last years. The appearance of Deep Learning algorithms and Convolutional Neural Networks, altogether with the increased processing ...

    Effective methods for recurrence solutions in delay differential equations 

    Gimeno i Alquézar, Joan (Fecha de defensa: 2020-01-08)

    This thesis deals with the jet transport for numerical integrators and the effective invariant object computation of delay differential equations. Firstly we study how automatic differentiation (AD) affects when ...

    Efficient and convergent natural gradient based optimization algorithms for machine learning 

    Sánchez López, Borja (Fecha de defensa: 2022-11-22)

    [eng] Many times machine learning is casted as an optimization problem. This is the case when an objective function assesses the success of an agent in a certain task and hence, learning is accomplished by optimizing that ...

    End-to-End AI Solutions for Capsule Endoscopy: Enhancing Efficiency and Accuracy in Gastrointestinal Diagnostics 

    Gilabert Roca, Pere (Fecha de defensa: 2025-01-22)

    [eng] Artificial Intelligence (AI) models are fundamentally transforming the way clinicians carry out their daily tasks. By streamlining various processes, AI offers a more robust and consistent method for reviewing ...

    Evolutionary Bags of Space-Time Features for Human Analysis 

    Ponce López, Víctor (Fecha de defensa: 2016-06-02)

    The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. ...