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新SCALLOP方法将Boltzmann生成器速度提升10倍

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为SCAlable LikeLihood distillation of flOw maPs (SCALLOP) 的新方法来训练Boltzmann生成器。该方法基于F2D2模型，但引入了一种更具可扩展性、无Hutchinson的似然蒸馏目标，可以进行向量化。SCALLOP在分子科学和图像数据集上显示出有效性，显著降低了训练方差和时间，同时提高了性能，并提供了比现有方法高达10倍的推理速度提升。 AI

影响 这种新方法为Boltzmann生成器提供了显著的速度提升，有可能加速分子科学和其他领域的研究。

排序理由 该集群包含一篇详细介绍生成模型新方法的学术论文。

在 arXiv stat.ML 阅读 →

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新SCALLOP方法将Boltzmann生成器速度提升10倍

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · RuiKang OuYang, Hanlin Yu, Xinyue Ai, Yutong He, Nicholas M. Boffi, Pradeep Ravikumar, Jose Miguel Hernandez-Lobato, Max Simchowitz, Benjamin Kurt Miller, Omar Chehab ·

    通过可扩展似然流图实现少步玻尔兹曼生成器

    arXiv:2606.29110v1 Announce Type: cross Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating t…

  2. arXiv stat.ML TIER_1 English(EN) · Omar Chehab ·

    通过可扩展似然流图实现少步玻尔兹曼生成器

    Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating the model likelihood. In particular, most existing …

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