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English(EN) Particle GFlowNets: Rethinking Generative Marginalization Models

Particle GFlowNets 统一生成模型，加速训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新方法 Particle GFlowNets，它将生成边际化模型 (MaMs) 与生成流网络 (GFlowNets) 统一起来。这种新方法通过实现更快的后验评估并将其采样策略扩展到非自回归生成过程来增强 MaMs。通过纳入源自 Gelman-Rubin 统计量用于全状态 पुनर्जीवन (rejuvenation) 的标准，Particle GFlowNets 显著加速了在大型组合空间中的训练收敛，实验证明了这一点。 AI

影响 加速生成模型在大型组合空间中的训练。

排序理由 该集群包含一篇详细介绍新方法及其实验验证的学术论文。[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 →

Particle GFlowNets 统一生成模型，加速训练

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该集群包含一篇详细介绍新方法及其实验验证的学术论文。[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) · Tiago da Silva, Diego Mesquita, Salem Lahlou ·

    Particle GFlowNets：重新思考生成式边缘化模型

    arXiv:2609.11538v1 Announce Type: new Abstract: Generative Marginalization Models (MaMs) have been recently introduced as efficient neural sampling models for any-order autoregressive modelling of discrete distributions. By learning both the marginal and conditional probabilities…

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