End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Interpretability is essential for machine learning models to be trusted and deployed in critical
domains. However, existing methods for interpreting text models are often complex, lack …

Dimensionality reduction is a crucial area in artificial intelligence that enables the
visualization and analysis of high-dimensional data. The main use of dimensionality …

Depression is a prevalent mental disorder that affects a significant portion of the global
population. Despite recent advancements in EEG-based depression recognition models …

Multilabel data comprise instances associated with multiple binary target variables. The
main learning task from such data is multilabel classification, where the goal is to output a …

Attention-based architectures, in particular transformers, are at the heart of a technological
revolution. Interestingly, in addition to helping obtain state-of-the-art results on a wide range …

This paper discusses the critical issue of enhancing the explainability and performance of
Large Language Models (LLMs) in the biomedical domain by leveraging the structural …

Transformers are widely recognized as leading models for NLP tasks due to their attention-
based architecture. However, their complexity and numerous parameters hinder the …