Neural networks (NNs) often assign high confidence to their predictions, even for points far out of distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model ...

The uncertainty related to physical parameters is a major challenge in numerical modeling. However, due to the large number of such parameters in numerical models, reducing the uncertainty for all of ...

Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if ...

A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. Pollsters trying to predict presidential election results ...

Tyche is a machine-learning framework that can generate plausible answers when asked to identify potential disease in medical images. By capturing the ambiguity in images, the technique could prevent ...

To effectively protect biodiversity in an era of climate change, ecologists first have to know where animal and plant species are located and then be able to predict what habitats will be available to ...