The advent of Transformer-based (i.e., based on self-attention architectures) language models has revolutionized the entire field of Natural Language Processing (NLP). Once pre-trained on large, unlabelled corpora, we can ...

This thesis is focused on exploring explainable AI algorithms and in particular Layer-Wise Relevance Propagation (LRP). We explore different LRP rules and analyze the values inside the network to understand the saliency ...

Incorporation of ethical values in the field of Artificial Intelligence is inevitable. With the rapid development of technologies capable of making autonomous decisions, more attention should be dedicated to the process ...

Abnormalities of cardiac rhythm are associated with substantial morbidity and economic costs. Atrial Fibrillation is the most common cardiac electrophysiology disorder worldwide affecting around 2.3 million people just in ...

The purpose of this project is to design a general system for emotion recognition through social signals in dyadic using deep learning methods using raw data from audio, video and text transcriptions from publicly available ...

Applications of deep reinforcement learning in multi-agent systems are a rapidly developing scientific field that focuses on constructing computational frameworks and models for how various agents can interact efficiently ...

In this work, we proposed a new approach for learning legged locomotion for any legged robot, in the sagittal plane, by using a combination of classical control techniques and reinforcement learning. Specifically, we use ...

Estimating and forecasting passenger demand is one of the major applications for Intelligent Transport Systems (ITS) data sources. Forecasting public bus transport occupations can be useful in order to improve user comfort ...