English(EN) Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

新的FLAPS框架统一了函数空间回归和逆问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员推出了一种新颖的函数空间后验采样框架——流退火后验采样（FLAPS）。该方法通过利用预训练的函数空间流匹配先验，统一了随机过程回归和PDE逆问题。FLAPS允许从稀疏和噪声数据中进行似然引导的推理，处理可变的查询离散化，并且显著避免了显式的先验密度评估。 AI

影响这个新框架有望改善科学逆问题和随机过程回归中的不确定性量化和采样效率。

排序理由该集群包含一篇研究论文，详细介绍了一种用于函数空间回归和逆问题的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Yaozhong Shi, Zachary E. Ross, Yisong Yue · 2026-10-01 04:00

面向函数空间回归和逆问题的流动退火后验采样

arXiv:2606.22346v2 Announce Type: replace-cross Abstract: Principled regression for stochastic processes is a long-standing challenge with deep connections to scientific inverse problems. We introduce Flow Annealing Posterior Sampling (FLAPS), to our knowledge the first function-…