English(EN) ESS-Flow: Training-free guidance as Bayesian inference in source space

ESS-Flow 方法为生成模型实现无训练引导

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员推出 ESS-Flow，一种无需重新训练即可引导预训练流生成模型的新颖方法。该技术通过椭圆切片采样在源空间中利用贝叶斯推断，利用了这些模型固有的高斯先验。ESS-Flow 仅需要通过生成和观测过程的正向传播，使其适用于梯度不可用的任务，例如不可微模拟或量化。该方法在科学应用中显示出潜力，包括生成具有特定性质的材料、具有增强可设计性的蛋白质结构以及类药物分子。 AI

影响在科学发现中，当梯度可能不可用时，可以更灵活地应用生成模型。

排序理由这是一篇详细介绍生成模型新方法的论文。[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 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Adhithyan Kalaivanan, Zheng Zhao, Jens Sj\"olund, Fredrik Lindsten · 2026-11-05 05:00

ESS-Flow：源空间中无需训练的引导作为贝叶斯推断

arXiv:2510.05849v2 Announce Type: replace Abstract: Guiding pretrained flow-based generative models for conditional generation or to produce samples with desired target properties enables solving diverse tasks without retraining on paired data. We present ESS-Flow, a gradient-fre…

本文如何被排名

报道来源 [1]

ESS-Flow：源空间中无需训练的引导作为贝叶斯推断

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