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RED-KLwSGS:动能Langevin加速扩散先验后验采样

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2026年10月8日,研究者在arXiv(Statistics Machine Learning)发布RED-KLwSGS方法,用于扩散先验下成像逆问题的加速后验采样。该方法在Split Gibbs采样框架中保留数据变量的精确高斯更新,同时用单次去噪得分驱动的欠阻尼(动能)Langevin扩散更新辅助变量。研究者称其单次迭代成本与Langevin-within-SGS相当。这是目前该事件唯一报道,尚未见后续验证或对比结果。

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Oct 8, 2026
  1. arXiv · Statistics Machine Learning
    RED-KLwSGS:扩散先验下成像逆问题的加速后验采样

    研究者提出 RED-KLwSGS 采样方法,在 Split Gibbs 采样框架中保留数据变量的精确高斯更新,并用单次去噪得分驱动的欠阻尼(动能)Langevin 扩散更新辅助变量,单次迭代成本与 Langevin-within-SGS 相当。

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