Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue.
Uncertainty quantification is required for many applications, and disentangled aleatoric and …

Abstract We show that Deep Neural Networks introduce two heteroscedastic Gumbel noise
sources into Q-Learning. To account for these noise sources, we propose Double Gumbel Q …

Characterizing uncertainty in machine learning models has recently gained interest in the
context of machine learning reliability, robustness, safety, and active learning. Here, we …

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 …

Flexibly quantifying both irreducible aleatoric and model-dependent epistemic uncertainties
plays an important role for complex regression problems. While deep neural networks in …

Clouds and haze often occlude optical satellite images, hindering continuous, dense
monitoring of the Earth's surface. Although modern deep learning methods can implicitly …

Quantifying uncertainty in machine learning is important in new research areas with scarce
high-quality data. In this work, we develop an explainable uncertainty quantification method …

Abstract Model-based offline reinforcement learning (RL) aims to find highly rewarding
policy, by leveraging a previously collected static dataset and a dynamics model. While the …

Human pose estimation (HPE) is integral to scene understanding in numerous safety-critical
domains involving human-machine interaction, such as autonomous driving or semi …

Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature
of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG) …