反问题变分推理中的后验温度优化
Posterior Temperature Optimization in Variational Inference for Inverse Problems
摘要：
贝叶斯方法具有解决逆问题的有用特性，例如层析重建。先验分布引入了正则化，这有助于解决不适定问题并减少过拟合。在实践中，这通常会导致次优的后验温度，并且贝叶斯方法的全部潜力没有实现。在本文中，我们使用贝叶斯优化优化先验分布和后验温度的参数。回火后验导致更好的预测性能和改进的不确定性校准，我们证明稀疏视图CT重建的任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
