mlsanityData to 14 Oct 2026

Proximal Residual Flows for Bayesian Inverse Problems

arXiv 2211.17158 ↗First posted 30 Nov 2022cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 35 of its 46 references that are in the map. First posted on arXiv 30 Nov 2022.

Builds on

35 of 46 references.

References · 35

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23 more
  1. 2018Invertible Residual Networkscited by 365
  2. 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 314
  3. 2019Residual Flows for Invertible Generative Modelingcited by 257
  4. 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 242
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  10. 2020Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimizationcited by 90
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  16. 2017Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problemscited by 40
  17. 2020Convolutional Proximal Neural Networks and Plug-and-Play Algorithmscited by 36
  18. 2021Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpointcited by 32
  19. 2020Stabilizing Invertible Neural Networks Using Mixture Modelscited by 30
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.

  1. 2023Manifold Learning by Mixture Models of VAEs for Inverse Problemscited by 17
  2. 2024Importance Corrected Neural JKO Samplingcited by 7
  3. 2024Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Propertiescited by 2
  4. 2024Mixed Noise and Posterior Estimation with Conditional DeepGEMcited by 1
  5. 2026Task Switching Without Forgetting via Proximal Decouplingcited by 0