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bayesian methods 1deep learning 1ensembles 1out-of-distribution detection 1uncertainty quantification 1

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stat.ML2026

Uncertainty quantification for trustworthy deep learning: Methods and measures

H. Martin Gillis, Thomas Trappenberg

The paper surveys methods for quantifying uncertainty in deep neural networks, focusing on ensemble-based and approximate Bayesian approaches and how their outputs are measured.