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cs.LG2026
Noise, Denoise, Correct: MCMC Posterior Sampling with Diffusion Priors in Three Steps
So Takao, Gregory David Bellchambers, Luke Ye +2
Pretrained diffusion models are powerful priors for inverse problems, but posterior sampling under nonlinear, non-differentiable forward models remain hard. We introduce diffusion…
cs.LG2025
Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators
Albert Matveev, Sanmitra Ghosh, Aamal Hussain +2
Operator learning is a powerful paradigm for solving partial differential equations, with Fourier Neural Operators serving as a widely adopted foundation. However, FNOs face signif…