English(EN) Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs

新采样方法利用概率流ODE处理复杂分布

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-10 04:00

研究人员提出了一种利用线性随机插值推导出的概率流常微分方程（ODEs）来采样玻尔兹曼密度的全新方法。该技术采用一系列Langevin采样器来模拟流，从而能够高效生成中间样本并稳健地估计速度场。该方法在复杂多模态分布的数值实验和贝叶斯推断任务中已证明了其有效性。 AI

影响引入了一种从复杂分布采样的创新方法，有望提高贝叶斯推断及相关AI任务的效率。

排序理由这是一篇详细介绍概率分布采样新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Chenguang Duan, Yuling Jiao, Gabriele Steidl, Christian Wald, Jerry Zhijian Yang, Ruizhe Zhang · 2026-08-10 04:00

基于Langevin的流ODE中速度和初始化估计的随机插值采样

arXiv:2601.08527v3 Announce Type: replace-cross Abstract: We propose a novel method for sampling from unnormalized Boltzmann densities based on a probability flow ordinary differential equation (ODE) derived from linear stochastic interpolants. The key innovation of our approach …