Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an
alternative communication channel to patients with severe motor disabilities, achieving high …

Fast estimates of model uncertainty are required for many robust robotics applications. Deep
Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still …

Uncertainty decomposition refers to the task of decomposing the total uncertainty of a model
into data (aleatoric) uncertainty, resulting from the inherent complexity or ambiguity of the …

In the past couple of years, various approaches to representing and quantifying different
types of predictive uncertainty in machine learning, notably in the setting of classification …

Deep learning (DL), which involves powerful black box predictors, has achieved a
remarkable performance in medical image analysis, such as segmentation and classification …

Evidential deep learning, built upon belief theory and subjective logic, offers a principled
and computationally efficient way to turn a deterministic neural network uncertainty-aware …

Early diagnosis of retinopathy is essential for preventing retinal complications and visual
impairment due to diabetes. For the detection of retinopathy lesions from retinal images …

Quantifying uncertainty is important for actionable predictions in real-world applications. A
crucial part of predictive uncertainty quantification is the estimation of epistemic uncertainty …

Knowing when a machine learning system is not confident about its prediction is crucial in
medical domains where safety is critical. Ideally, a machine learning algorithm should make …

The global ambitions of a carbon-neutral society necessitate a stable and robust smart grid
that capitalizes on frequency reserves of renewable energy. Frequency reserves are …