Hilbert-Scale Stability of Posterior Diffusion Priors for Ill-Posed Signal Recovery
arXiv:2610.27030v1 Announce Type: new Abstract: Indirect and noisy observations are difficult to invert when the forward operator smooths out the unknown signal. In this case, the unstable modes are precisely the modes that the data reveals least, thus the success of any reconstruction method depends on how prior information interacts with the geometry of the operator across regularity scales. Diffusion priors have become ubiquitous for such problems; however, the current theory still does not determine when a learned score remains informative under ill-posed forward operators. In this paper,
阅读原文 · arXiv 机器学习本文由 SoccerDream AI 自动聚合索引，内容版权归原作者与 arXiv 机器学习 所有。
本站仅作技术动态聚合，不构成任何形式的建议。
本站仅作技术动态聚合，不构成任何形式的建议。