English(EN) Your GFlowNet Secretly Learns an Optimal Transport Plan

GFlowNets 被证明可学习最优传输方案

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-04 15:14

研究人员建立了生成流网络 (GFlowNets) 与最优传输 (OT) 之间的理论联系。他们的工作表明，非循环 GFlowNets 在优化后可以有效地编码最优传输方案。这种联系使得 GFlowNet 框架能够通过神经参数化和边流应用于大规模 OT 问题，实验表明与现有的 OT 求解器一致。 AI

影响为将 GFlowNets 应用于最优传输问题建立了新的理论框架，有可能提高大规模图分析的效率。

排序理由该集群包含一篇详细介绍 GFlowNets 与最优传输之间理论联系的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Ian Maksimov, Nikita Morozov, Denis Belomestny, Sergey Samsonov · 2026-06-05 04:00

你的 GFlowNet 已秘密学会最优传输方案

arXiv:2606.06272v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) are a framework for sampling structured objects via stochastic trajectories in a directed graph. In this work, we establish a theoretical connection between non-acyclic GFlowNets and optimal tran…
arXiv cs.AI TIER_1 English(EN) · Sergey Samsonov · 2026-06-04 15:14

你的 GFlowNet 暗中学习最优传输方案

Generative Flow Networks (GFlowNets) are a framework for sampling structured objects via stochastic trajectories in a directed graph. In this work, we establish a theoretical connection between non-acyclic GFlowNets and optimal transport (OT). We show that fixing the initial flow…