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Group project for the Bayesian Machine Learning class at ENS Paris Saclay MVA program 2024: Sequential Monte Carlo sampler for bayesian blind inverse problems using diffusion models

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Sequential Monte Carlo Sampling of Diffusion Models

Joint project work with Simon Queric

Structure of the repository

MCGDiff_GMM: Monte Carlo Guided Diffusion for bayesian linear inverse problems on a Gaussian Mixture Model

MCGDiff sampling

MCGDiff for inpainting

TDS_GMM: Twisted Diffusion Sampling for (blind) inverse problems on a Gaussian Mixture Model

TDS_images: Twisted Diffusion Sampling for (blind) inverse problems on images

TDS sampling

TDS sampling for super-resolution.

TDS_cosmo: Twisted Diffusion Sampling for a cosmological component separation problem

TDS sampling

TDS sampling for component separation.

Requirements

Depending on the specific folder, the requirements are different.

References

[1] G. Cardoso, Y. J. E. Idrissi, S. L. Corff, and E. Moulines. Monte carlo guided diffusion for bayesian linear inverse problems, 2023.

[2] H. Chung, J. Kim, M. T. Mccann, M. L. Klasky, and J. C. Ye. Diffusion posterior sampling for general noisy inverse problems, 2023.

[3] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models, 2020.

[4] J. Song, A. Vahdat, M. Mardani, and J. Kautz. Pseudoinverse-guided diffusion models for inverse problems. In International Conference on Learning Representations, 2023.

[5] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole. Score-based generative modeling through stochastic differential equations, 2021.

[6] L. Wu, B. L. Trippe, C. A. Naesseth, D. M. Blei, and J. P. Cunningham. Practical and asymptoti- cally exact conditional sampling in diffusion models, 2023

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Group project for the Bayesian Machine Learning class at ENS Paris Saclay MVA program 2024: Sequential Monte Carlo sampler for bayesian blind inverse problems using diffusion models

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