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Posterior Temperature Optimization in Variational Inference for Inverse Problems
Max Laves · Malte Tölle · Alexander Schlaefer · Sandy Engelhardt
Event URL: https://openreview.net/forum?id=09O1tHrEEFY »
Bayesian methods feature useful properties for solving inverse problems, such as tomographic reconstruction. The prior distribution introduces regularization, which helps solving the ill-posed problem and reduces overfitting. In practice, this often results in a suboptimal posterior temperature and the full potential of the Bayesian approach is not realized. In this paper, we optimize both the parameters of the prior distribution and the posterior temperature using Bayesian optimization. Well-tempered posteriors lead to better predictive performance and improved uncertainty calibration, which we demonstrate for the task of sparse-view CT reconstruction.
Author Information
Max Laves (Hamburg University of Technology)
Malte Tölle
Alexander Schlaefer (Hamburg University of Technology)
Sandy Engelhardt (University Hospital Heidelberg)
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