1. On calibration of modern neural networks;Guo,2017
2. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods;Platt;Advances in large margin classifiers,1999
3. Dropout as a bayesian approximation: Representing model uncertainty in deep learning;Gal,2016
4. Simple and scalable predictive uncertainty estimation using deep ensembles;Lakshminarayanan;Advances in neural information processing systems,2017
5. Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift;Ovadia;Advances in neural information processing systems,2019