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English(EN) SNACK: A Sequential Notation Framework for Probabilistic Graph Generation

新的SNACK框架使序列模型能够进行概率图生成

研究人员开发了SNACK,一个新颖的、用于概率图生成的序列表示框架。该框架通过将图线性化为字符串表示,使序列模型能够生成并有效地建模图。SNACK通过强制执行约束来确保生成有效的图结构,并与传统的基于图的方法相比,提高了GFlowNets的训练吞吐量。该框架在标准的图生成任务和分子分布学习基准测试中展示了具有竞争力的样本质量。 AI

影响 该框架可以增强序列模型生成复杂图结构的能力,可能对药物发现和材料科学等领域产生影响。

排序理由 该集群包含一篇详细介绍新图生成框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 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SNACK框架使序列模型能够进行概率图生成

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Tool
该集群包含一篇详细介绍新图生成框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hohyun Kim, Hyesung Kim, Min-hwan Oh, Seunggeun Lee ·

    SNACK:用于概率图生成的序列化表示框架

    arXiv:2610.19781v1 Announce Type: cross Abstract: We introduce SNACK, a string representation for probabilistic graph generation that enables sequence models to generate and model graphs with discrete node and edge features. SNACK linearizes graphs by encoding the nonzero entries…