Intrinsic uncertainties and where to find them
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Abstract (arXiv)
We introduce a framework for uncertainty estimation that both describes and extends many existing methods. We consider typical hyperparameters involved in classical training as random variables and marginalise them out to capture various sources of uncertainty in the parameter space. We investigate which forms and combinations of marginalisation are most useful from a practical point of view on standard benchmarking data sets. Moreover, we discuss how some marginalisations may produce reliable estimates of uncertainty without the need for extensive hyperparameter tuning and/or large-scale ensembling.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 6 Jul 2021.
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References · 11
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
- 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 555
- 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
- 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
- 2020Hyperparameter Ensembles for Robustness and Uncertainty Quantificationcited by 149
- 2019A General Framework for Uncertainty Estimation in Deep Learningcited by 119
- 2018Lightweight Probabilistic Deep Networkscited by 77
- 2019TRADI: Tracking deep neural network weight distributions for uncertainty estimationcited by 39
- 2016Bayesian Hyperparameter Optimization for Ensemble Learningcited by 11
- 2020Single Shot MC Dropout Approximationcited by 10
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