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新的GEM框架推动了用于分子发现的图生成

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为Graph Energy Matching (GEM) 的新生成建模框架，专为分子图等离散数据设计。GEM利用置换不变势能来引导噪声向高似然图区域传输，并在这些区域内优化样本。该方法旨在克服离散能量模型中常见的采样效率低下和训练不稳定问题，在分子图基准测试中达到或超过了当前离散扩散基线的性能。 AI

影响 引入了一个新颖的离散生成建模框架，有望改进分子发现和材料设计。

排序理由 这是一篇描述新模型/框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新的GEM框架推动了用于分子发现的图生成

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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Michal Balcerak, Suprosana Shit, Chinmay Prabhakar, Sebastian Kaltenbach, Michael S. Albergo, Yilun Du, Bjoern Menze ·

    Graph Energy Matching: 传输对齐的基于能量的模型用于图生成

    arXiv:2603.23398v2 Announce Type: replace-cross Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, probabilistic inference is particularly val…

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