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

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Large language models (LLMs) specializing in natural language generation (NLG) have
recently started exhibiting promising capabilities across a variety of domains. However …

People supported by AI-powered decision support tools frequently overrely on the AI: they
accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the …

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

Convolutional image classifiers can achieve high predictive accuracy, but quantifying their
uncertainty remains an unresolved challenge, hindering their deployment in consequential …

Machine Learning in Medicine In this view of the future of medicine, patient–provider
interactions are informed and supported by massive amounts of data from interactions with …

The increasing prevalence of digitised workflows in diagnostic pathology opens the door to
life-saving applications of artificial intelligence (AI). Explainability is identified as a critical …

In this paper, we propose Patience-based Early Exit, a straightforward yet effective inference
method that can be used as a plug-and-play technique to simultaneously improve the …