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English(EN) Scalable Algorithms for Approximate DNF Model Counting

新的蒙特卡洛算法为DNF模型计数提供数量级的加速

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的蒙特卡洛算法，用于对析取范式（DNF）公式进行近似模型计数。这种新方法包含自适应停止规则和高效的短路公式评估。该算法被证明可以实现概率近似正确（PAC）学习界，并与现有方法相比显示出优越的渐近效率。实验结果表明，其性能比以前的算法提高了几个数量级，能够处理数百万变量的问题。 AI

影响 这项研究可以显著提高依赖于DNF公式的AI系统中概率推理和查询评估的效率。

排序理由 该集群包含一篇详细介绍新算法及其实验验证的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的蒙特卡洛算法为DNF模型计数提供数量级的加速

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该集群包含一篇详细介绍新算法及其实验验证的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Paul Burkhardt, David G. Harris, Kevin T Schmitt ·

    可扩展的近似DNF模型计数算法

    arXiv:2601.10511v2 Announce Type: replace-cross Abstract: Model counting of Disjunctive Normal Form (DNF) formulas is a critical problem in applications such as probabilistic inference and network reliability. For example, it is often used for query evaluation in probabilistic da…

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