Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …

For image segmentation, the current standard is to perform pixel-level optimization and
inference in Euclidean output embedding spaces through linear hyperplanes. In this work …

Ensembles over neural network weights trained from different random initialization, known
as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently …

Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs). The
ability to model and forecast remaining useful life of these batteries enables UAV reliability …

Bayesian neural networks (BNNs) demonstrate promising success in improving the
robustness and uncertainty quantification of modern deep learning. However, they generally …

Despite the recent developments in deep learning models, their applications in clinical
decision-support systems have been very limited. Recent digitalisation of health records …

Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of
large language models (LLMs). However, fine-tuned LLMs often become overconfident …

Quality Estimation (QE) is an important component in making Machine Translation (MT)
useful in real-world applications, as it is aimed to inform the user on the quality of the MT …