论文标题
贝叶斯逆问题的近端残留流
Proximal Residual Flows for Bayesian Inverse Problems
论文作者
论文摘要
归一化流是贝叶斯反问题中生成建模，密度估计和后重建的强大工具。在本文中，我们介绍了近端残留流，这是一种正常化流量的新结构。根据这一事实，近端神经网络是定义的平均操作员，我们确保某些残留块的可逆性。此外，我们将体系结构扩展到有条件的近端残留流，用于贝叶斯反问题内的后验重建。我们证明了在数值示例上近端残留流的性能。
Normalizing flows are a powerful tool for generative modelling, density estimation and posterior reconstruction in Bayesian inverse problems. In this paper, we introduce proximal residual flows, a new architecture of normalizing flows. Based on the fact, that proximal neural networks are by definition averaged operators, we ensure invertibility of certain residual blocks. Moreover, we extend the architecture to conditional proximal residual flows for posterior reconstruction within Bayesian inverse problems. We demonstrate the performance of proximal residual flows on numerical examples.
