Papers › Proximal Residual Flows for Bayesian Inverse Problems

Proximal Residual Flows for Bayesian Inverse Problems

30 Nov 2022arXiv:2211.17158archive 2025-07-28

Johannes Hertrich

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.

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