Cold posteriors have been reported to perform better in practice in the context of Bayesian deep learning ( Wenzel2020 et al., 2020). In variational inference, it is common to employ only a partially tempered posterior by scaling the complexity term in the log-evidence lower bound (ELBO). In this work, we first derive the ELBO for a fully tempered posterior in mean-field variational inference and subsequently use Bayesian optimization to automatically find the optimal posterior temperature  . Choosing an appropriate posterior temperature leads to better predictive performance and improved uncertainty calibration, which we demonstrate for the task of denoising medical X-ray images.
Cold posteriors have been reported to perform better in practice in the context of Bayesian deep learning ( Wenzel et al., 2020). In variational inference, it is common to employ only a partially tempered posterior by scaling the complexity term in the log-evidence lower bound (ELBO). In this work, we optimize the ELBO for a fully tempered posterior in mean-field variational inference and use Bayesian optimization to automatically find the optimal posterior temperature and prior scale . Choosing an appropriate posterior temperature leads to better predictive performance and improved uncertainty calibration, which we demonstrate for the task of denoising medical X-ray images.
R
Wenzel2020
Wenzel
R
first derive
optimize
D
subsequently
A
and prior scale