S16 (2024-2025)

Large language models (LLMs) have emerged as strong contenders in machine translation. Yet, they often fall behind specialized neural machine translation systems in addressing discourse phenomena, such as pronoun resolution and lexical cohesion at the document level. In the seminar, … Read More

Today’s overburdened health systems face numerous challenges, exacerbated by an aging population. Speech emerges as a ubiquitous biomarker with strong potential for the development of low-cost, remote testing tools for several diseases. In fact, speech encodes information about a plethora … Read More

Neural networks are often overconfident about their predictions, which undermines their reliability and trustworthiness. In this presentation, I will present our work entitled Error-Driven Uncertainty Aware Training (EUAT), which aims to enhance the ability of neural classifiers to estimate their … Read More

Evaluating image captions is essential for ensuring both linguistic fluency and accurate semantic alignment with visual content. While reference-free metrics such as CLIPScore have advanced automated caption evaluation, most existing work on learned evaluation metrics remains limited to pointwise English-centric … Read More

Adaptive sparse attention mechanisms have emerged as a powerful alternative to dense attention in transformers, offering more interpretability for sequence modeling. Despite this, their widespread adoption has been limited by computational inefficiencies and insufficient understanding of their theoretical properties compared … Read More