mlsanityData to 7 Oct 2026

Mixed Noise and Posterior Estimation with Conditional DeepGEM

arXiv 2402.02964 ↗First posted 5 Feb 2024cs.LG physics.data-ancited by 1

Not on the map. Why?

Abstract (arXiv)

Motivated by indirect measurements and applications from nanometrology with a mixed noise model, we develop a novel algorithm for jointly estimating the posterior and the noise parameters in Bayesian inverse problems. We propose to solve the problem by an expectation maximization (EM) algorithm. Based on the current noise parameters, we learn in the E-step a conditional normalizing flow that approximates the posterior. In the M-step, we propose to find the noise parameter updates again by an EM algorithm, which has analytical formulas. We compare the training of the conditional normalizing flow with the forward and reverse KL, and show that our model is able to incorporate information from many measurements, unlike previous approaches.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 69 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

33 of 69 references.

References · 33

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2014Conditional Generative Adversarial Netscited by 2,152
  5. 2016Density estimation using Real NVPcited by 2,029
  6. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  7. 2017Masked Autoregressive Flow for Density Estimationcited by 680
  8. 2019Neural Spline Flowscited by 590
  9. 2018Invertible Residual Networkscited by 364
  10. 2019Residual Flows for Invertible Generative Modelingcited by 256
  11. 2018Neural Autoregressive Flowscited by 207
  12. 2019Learning Likelihoods with Conditional Normalizing Flowscited by 170
21 more
  1. 2020Stochastic Normalizing Flowscited by 156
  2. 2019Guided Image Generation with Conditional Invertible Neural Networkscited by 147
  3. 2022Flow Annealed Importance Sampling Bootstrapcited by 124
  4. 2019Noise Flow: Noise Modeling with Conditional Normalizing Flowscited by 108
  5. 2021Annealed Flow Transport Monte Carlocited by 90
  6. 2019Block Neural Autoregressive Flowcited by 62
  7. 2020Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imagingcited by 57
  8. 2023Learning Interpolations between Boltzmann Densitiescited by 42
  9. 2023JANA: Jointly Amortized Neural Approximation of Complex Bayesian Modelscited by 41
  10. 2023Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolutioncited by 36
  11. 2021Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpointcited by 32
  12. 2019HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inferencecited by 31
  13. 2023Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernelcited by 25
  14. 2021Generalized Normalizing Flows via Markov Chainscited by 23
  15. 2022Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flowscited by 22
  16. 2023Score-Based Generative Models for PET Image Reconstructioncited by 22
  17. 2021Conditional Invertible Neural Networks for Medical Imagingcited by 19
  18. 2021Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescencecited by 12
  19. 2022WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolutioncited by 10
  20. 2022Proximal Residual Flows for Bayesian Inverse Problemscited by 5
  21. 2018Large Scale Clustering with Variational EM for Gaussian Mixture Modelscited by 4

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Provable Mixed-Noise Learning with Flow-Matchingcited by 2