AI RESEARCH
Learning Normalized Energy Models for Linear Inverse Problems
arXiv CS.LG
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ArXi:2605.15487v1 Announce Type: new Generative diffusion models can provide powerful prior probability models for inverse problems in imaging, but existing implementations suffer from two key limitations: $(i)$ the prior density is represented implicitly, and $(ii)$ they rely on likelihood approximations that