Language models (LMs) have demonstrated remarkable capabilities across a wide range of
tasks in various domains. Despite their impressive performance, the reliability of their output …

Large language models (LLMs) have demonstrated remarkable capabilities across a wide
range of tasks in various domains. Despite their impressive performance, they can be …

Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such
as active learning, misclassification detection, adversarial attack detection, out-of-distribution …

Many text classification tasks are inherently ambiguous, which results in automatic systems
having a high risk of making mistakes, in spite of using advanced machine learning models …

Annotated data is an essential ingredient in natural language processing for training and
evaluating machine learning models. It is therefore very desirable for the annotations to be …

Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that
incorporates progressive changes in patient anatomy into active plan/dose adaption during …

Large language models (LLMs) excel in providing natural language responses that sound
authoritative, reflect knowledge of the context area, and can present from a range of varied …

Recent advancements in the capabilities of large language models (LLMs) have paved the
way for a myriad of groundbreaking applications in various fields. However, a significant …

Deep learning-based methods for cardiac MR segmentation have achieved state-of-the-art
results. However, these methods can generate incorrect segmentation results which can …

Transformers are state-of-the-art in a wide range of NLP tasks and have also been applied
to many real-world products. Understanding the reliability and certainty of transformer …