Français(FR) Fixed-point neural samplers on discrete spaces

新型神经网络采样器应对离散分布采样挑战

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员推出了一种离散Gibbs迭代神经网络采样器（Discrete Gibbs Iterative Neural Sampler），这是一种从离散、非归一化分布中采样的新方法，无需直接访问数据。该新方法通过采用不动点迭代，旨在克服现有离散神经网络采样器的局限性，如模式崩溃和缺乏收敛保证。该框架建立在掩码扩散技术之上，并扩展到分布传输，在高维系统中表现出可扩展性，并在合金相图等领域实现了准确的估计。 AI

影响为在离散数据上训练生成模型引入了一种更稳健的方法，有望改善需要复杂分布建模的领域的应用。

排序理由该集群包含一篇详细介绍新型神经网络采样算法的研究论文。[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 Français(FR) · Jiajun He, Denis Blessing, Mouyang Cheng, Yuanqi Du, Carles Domingo-Enrich · 2026-10-03 04:00

离散空间上的定点神经网络采样器

arXiv:2610.01739v1 Announce Type: new Abstract: Sampling from discrete, unnormalized distributions without access to data is a challenging problem. Neural samplers offer a promising approach by training generative models from density evaluations directly. Despite recent progress,…

本文如何被排名

报道来源 [1]

离散空间上的定点神经网络采样器

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