DEMAU: Decompose, Explore, Model and Analyse Uncertainties
Not on the map. Why?
Abstract (arXiv)
Recent research in machine learning has given rise to a flourishing literature on the quantification and decomposition of model uncertainty. This information can be very useful during interactions with the learner, such as in active learning or adaptive learning, and especially in uncertainty sampling. To allow a simple representation of these total, epistemic (reducible) and aleatoric (irreducible) uncertainties, we offer DEMAU, an open-source educational, exploratory and analytical tool allowing to visualize and explore several types of uncertainty for classification models in machine learning.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 12 Sep 2024.
Builds on
5 of 12 references.
References · 5
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
- 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
- 2020Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Countscited by 164
- 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 133
- 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
Built on by
Not cited yet.