[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Priors in bayesian deep learning: A review
V Fortuin - International Statistical Review, 2022 - Wiley Online Library
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 …
recent Bayesian deep learning models have often fallen back on vague priors, such as …
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …
combine data with mathematical laws in physics and engineering in a profound way …
Deep evidential regression
Deterministic neural networks (NNs) are increasingly being deployed in safety critical
domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this …
domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this …
How good is the Bayes posterior in deep neural networks really?
F Wenzel, K Roth, BS Veeling, J Świątkowski… - ar** deep neural networks with uncertainty estimates
Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The
Bayesian framework provides a principled way of uncertainty estimation but is often not …
Bayesian framework provides a principled way of uncertainty estimation but is often not …
Deup: Direct epistemic uncertainty prediction
Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes
with more evidence. While existing work focuses on using the variance of the Bayesian …
with more evidence. While existing work focuses on using the variance of the Bayesian …
A simple approach to improve single-model deep uncertainty via distance-awareness
Accurate uncertainty quantification is a major challenge in deep learning, as neural
networks can make overconfident errors and assign high confidence predictions to out-of …
networks can make overconfident errors and assign high confidence predictions to out-of …