A decoupled Bayesian neural network that maps a DNN's logits to calibrated probabilities consistently reduces expected calibration error, at the cost of some accuracy loss on complex datasets.
Mikolov, et al., Efficient estimation of word representations in vector space, in: International Conference on Learning Representations, 2013
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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
A decoupled Bayesian neural network that maps a DNN's logits to calibrated probabilities consistently reduces expected calibration error, at the cost of some accuracy loss on complex datasets.