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English(EN) Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers

新熵方法提升生成模型样本质量

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了一种优化生成模型离散化方法，旨在用有限的计算资源提高样本质量。这种方法被称为条件边际熵率目标，将概率路径的几何形状与边际分布的演变分离开来。将其应用于流匹配和薛定谔桥模型，在样本质量指标（如MMD和FID）方面显示出显著的改进，尤其是在低样本量情况下，并有望应用于蛋白质生成等领域。 AI

影响 以更少的计算步骤提高生成模型的样本质量，可能加速研究和应用开发。

排序理由 该集群包含两篇详细介绍生成模型和信息论新研究的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新熵方法提升生成模型样本质量

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Luca Ambrogioni ·

    跨桥熵：用于流和薛定谔采样器的条件-边际离散化

    For a fixed flow-based generative model under a small inference budget, sample quality can depend strongly on where the sampler spends its few function evaluations. Flow matching and Schrödinger bridges define probability paths, yet their inference grids are usually heuristic or …

  2. arXiv stat.ML TIER_1 English(EN) · Shahab Asoodeh, Jun Chen ·

    打破熵耦合中的有限样本障碍

    arXiv:2605.16229v1 Announce Type: cross Abstract: Dependence among marginally constrained observations can break a finite-sample barrier. To formalize this phenomenon, we introduce the \emph{minimum list entropy coupling} $H(P\|Q_1,\dots,Q_m)$, the minimum conditional entropy $H(…

  3. arXiv stat.ML TIER_1 English(EN) · Jun Chen ·

    打破熵耦合中的有限样本障碍

    Dependence among marginally constrained observations can break a finite-sample barrier. To formalize this phenomenon, we introduce the \emph{minimum list entropy coupling} $H(P\|Q_1,\dots,Q_m)$, the minimum conditional entropy $H(X|Y_1,\dots,Y_m)$ over all joint distributions wit…

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