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Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding

arXiv:2609.30539v1 Announce Type: cross Abstract: Split Gibbs sampling enables diffusion posterior inference for general nonlinear inverse problems by decoupling prior and likelihood computations, allowing a pretrained diffusion prior to be reused across measurement models. However, its likelihood…

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  1. 2026-09-28 04:00 · arXiv stat.ML
    Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding

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