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On Calibrated Model Uncertainty in Deep Learning
[article]
2022
arXiv
pre-print
Estimated uncertainty by approximate posteriors in Bayesian neural networks are prone to miscalibration, which leads to overconfident predictions in critical tasks that have a clear asymmetric cost or significant losses. Here, we extend the approximate inference for the loss-calibrated Bayesian framework to dropweights based Bayesian neural networks by maximising expected utility over a model posterior to calibrate uncertainty in deep learning. Furthermore, we show that decisions informed by
arXiv:2206.07795v1
fatcat:tmyodyo3mfgq7oybc6rnh6phyi