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Next Session - December 12 with Sylvain Le Corff
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All About That Bayes
Nov 6, 2023, 1:01:55 PM11/6/23
to All About That Bayes
Dear all,
As the CIRM just hosted a Bayesian autumn school this week, there is no session on November 7. The next seminar will take place on December, 12 at 16:00.
Sylvain Le Corff (LPSM, Sorbonne Université) will give a talk on Monte Carlo guided diffusions for Bayesian inverse problems.
The seminar will take place at SCAI (Sorbonne Université, Campus Pierre et Marie Curie) and will be available online via a zoom link
Best regards,
The All about that Bayes organising team
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Joint work with G. Cardoso, Y. Janati and E. Moulines
Abstract: Ill-posed linear inverse problems that combine knowledge of the forward measurement model with prior models arise frequently in various applications, from computational photography to medical imaging. Recent research has focused on solving these problems with score-based generative models (SGMs) that produce perceptually plausible images, especially in inpainting problems. In this study, we exploit the particular structure of the prior defined in the SGM to formulate recovery in a Bayesian framework as a Feynman--Kac model adapted from the forward diffusion model used to construct score-based diffusion. To solve this Feynman--Kac problem, we propose the use of Sequential Monte Carlo methods. The proposed algorithm, MCGdiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems.
As the CIRM just hosted a Bayesian autumn school this week, there is no session on November 7. The next seminar will take place on December, 12 at 16:00.
Sylvain Le Corff (LPSM, Sorbonne Université) will give a talk on Monte Carlo guided diffusions for Bayesian inverse problems.
The seminar will take place at SCAI (Sorbonne Université, Campus Pierre et Marie Curie) and will be available online via a zoom link
Best regards,
The All about that Bayes organising team
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Joint work with G. Cardoso, Y. Janati and E. Moulines
Abstract: Ill-posed linear inverse problems that combine knowledge of the forward measurement model with prior models arise frequently in various applications, from computational photography to medical imaging. Recent research has focused on solving these problems with score-based generative models (SGMs) that produce perceptually plausible images, especially in inpainting problems. In this study, we exploit the particular structure of the prior defined in the SGM to formulate recovery in a Bayesian framework as a Feynman--Kac model adapted from the forward diffusion model used to construct score-based diffusion. To solve this Feynman--Kac problem, we propose the use of Sequential Monte Carlo methods. The proposed algorithm, MCGdiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems.
All About That Bayes
Dec 6, 2023, 12:52:06 PM12/6/23
to All About That Bayes
Dear all,
Best regards,
The All about that Bayes organising team
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Joint work with G. Cardoso, Y. Janati and E. Moulines
Abstract: Ill-posed linear inverse problems that combine knowledge of the forward measurement model with prior models arise frequently in various applications, from computational photography to medical imaging. Recent research has focused on solving these problems with score-based generative models (SGMs) that produce perceptually plausible images, especially in inpainting problems. In this study, we exploit the particular structure of the prior defined in the SGM to formulate recovery in a Bayesian framework as a Feynman--Kac model adapted from the forward diffusion model used to construct score-based diffusion. To solve this Feynman--Kac problem, we propose the use of Sequential Monte Carlo methods. The proposed algorithm, MCGdiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems.
The next seminar will take place on December, 12 at 16:00.
Sylvain Le Corff (LPSM, Sorbonne Université) will give a talk on Monte Carlo guided diffusions for Bayesian inverse problems.
Sylvain Le Corff (LPSM, Sorbonne Université) will give a talk on Monte Carlo guided diffusions for Bayesian inverse problems.
Note that the room has changed for this session, the seminar will take place in room 15-16-201, Sorbonne Université, Campus Pierre et Marie Curie, (between towers 15 and 16, second floor, room 201) and will be available online via a zoom link
Best regards,
The All about that Bayes organising team
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Joint work with G. Cardoso, Y. Janati and E. Moulines
Abstract: Ill-posed linear inverse problems that combine knowledge of the forward measurement model with prior models arise frequently in various applications, from computational photography to medical imaging. Recent research has focused on solving these problems with score-based generative models (SGMs) that produce perceptually plausible images, especially in inpainting problems. In this study, we exploit the particular structure of the prior defined in the SGM to formulate recovery in a Bayesian framework as a Feynman--Kac model adapted from the forward diffusion model used to construct score-based diffusion. To solve this Feynman--Kac problem, we propose the use of Sequential Monte Carlo methods. The proposed algorithm, MCGdiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems.
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