• Fine-turning open-source deep learning models for crowd tracking in immersive interactive installations

  Svetoslavov Hristov, Stefan (2024)

  This paper explores the development of a real-time people tracking system for immersive interactive environments using open-source deep learning models. The goal was to create an AI-based solution capable of tracking people ...
• The Impact of training order on memorization and forgetting in large language models

  Leybzon, Danny D. (2024)

  This paper investigates the phenomenon of memorization in large language models (LLMs), focusing on its dynamics throughout the training process and its implications for data privacy and copyright compliance. While LLMs ...
• Enhancing collaborative learning platforms: leveraging artificial intelligence for improved social presence

  Szafranek, Karolina Martyna (2024)

  Computer Supported Collaborative Learning (CSCL) supports the learning process by incorporating technologies that emphasize social interactions. A critical component of successful CSCL implementation is social presence, ...
• Imitation learning and policy representation for constrained reinforcement learning

  Caicoya Ros, Ana (2024)

  This thesis explores the integration of imitation learning and policy representation within the domain of constrained reinforcement learning (CRL) to enhance decision making in environments with stringent limitations. ...
• Refining 3D asset creation using machine learning and depth estimation

  Jiménez Ayguadè, Oriol (2024)

  This master’s thesis explores recent advancements in machine learning, particularly those enabling the generation of 3D graphics assets from textual descriptions or input images. Central to this research is the evaluation ...
• Exploring large language models for task planning in an open world

  Basbous, Adriana (2024)

  This thesis builds on the Common Sense-Based Open World Planning (COWP) framework, which integrates a classical task planner with an LLM module to enable robot autonomy in open world household environments. The framework ...
• A deep learning approach for the segmentation and classification of Plus disease in retinal fundus images of preterm infants

  Matamala Avrova, Elisabet-Vera (2024)

  Retinopathy of Prematurity (ROP) is a severe retinal vascular disorder affecting premature infants, characterized by abnormal vessel proliferation that can lead to vision impairment or blindness if untreated. Timely and ...
• Text embeddings for modeling the evolution of online discussions

  Wang, Qiongge (2024)

  The rise of online discussion platforms has transformed the way people communicate, exchange ideas, and engage with information. From social media platforms like Reddit, Twitter, and Facebook to specialized forums and ...
• Reasoning with language and knowledge graphs

  García Vázquez, Samuel (2024)

  The use of Large Language Models (LLMs) as general-purpose assistants is getting more widespread every day. Despite this, the deployment of these models in high-risk scenarios remains controversial due to issues such as ...
• Enhancing pyramidApp: a technological approach to facilitate computer-supported collaborative learning (CSCL) activities

  Moreno Arenas, Guillem (2024)

  This thesis explores the enhancement of PyramidApp, a tool designed to facilitate Computer-Supported Collaborative Learning (CSCL) activities. The research addresses the challenges and opportunities presented by digital ...
• Inverse reinforcement learning with linearly-solvable MDPs for multiple reward functions

  Deb, Ahana (2023-10-09)

  A subclass of Markov Decision Processes (MDPs), the Linearly solvable Markov Decision Processes (LMDPs), which have discrete state space and continuous control space, allow for a significant simplification of the inverse ...
• Predictive gut microbiome analysis for health assessment

  Gil Sorribes, Manel (2023-10-04)

  Personalized medicine is a rapidly evolving field to which many resources have been devoted recently. It represents a paradigm shift from a one-size-fits-all approach to healthcare, focusing instead on tailoring treatments ...
• Inverse optimal control for modeling virus mutations in SARS-CoV-2

  Meijer, Ilse (2022)

  Inverse Optimal Control (IOC) deals with the problem of recovering an unknown cost function in a Markov decision process from expert demonstrations acting optimally. In this thesis we apply IOC to SARS-CoV-2 data. For ...
• Extraction and categorization of Japanese lexical collocations with graph-aware transformers

  Nisho, Kosuke James (2022)

  Lexical collocations may be identified and categorized in context, which is helpful for language acquisition, dictionary creation, and many other downstream NLP tasks. However, the automatic collocation extraction and ...
• Taxonomic classification of metagenomic reads using machine learning models

  Alcobé Garcia, Eduard (2022)

  Microorganisms such as bacteria can be hard to identify correctly. Most current classification techniques are based on well conserved genes, for instance the 16S ribosomal RNA (16S rRNA). Nevertheless, achieving a ...
• Task and motion planning: scaling up

  Dalmau Moreno, Magí (2022)

  This master thesis contributes to provide planning capabilities to embodied agents so that they are able to decide what sequence of actions should be taken to solve a given complex problem, involving coupled reasoning ...
• A novel message passing approach to spatial air quality prediction in urban areas

  Calo Oliveira, Sergio (2022)

  Air pollution in our cities is a very significant cause of death and worsening of the quality of life in current times. Knowing in depth this phenomenon and its details and building tools that help us to mitigate its ...
• Linguistic emergence in deep neural networks

  Locatelli Metz, Davide (2022)

  Recent work in Emergent Communication has shown that deep neural networks can develop successful strategies of referential communication. Arguably, the extent to which they can do so depends on the properties of the ...
• Multilingual lexical simplification

  Pimienta Castillo, Jorge S. (2021-09)

  This report describes, implement, and evaluate one strategy for text simplification, namely, Lexical Simplification, that aims to reduce the complexity of some words in a sentence. This process is done in two main steps, ...
• Thompson sampling for prediction with expert advice

  Sánchez Pérez de Amézaga, Claudio (2021-09)

  We study Thompson Sampling for prediction with expert advice. With Follow the leader, and Follow the perturbed leader strategies, we present relevant results in order to proceed with Thompson Sampling Algorithm. Using a ...

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