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GFlowNets shown to learn optimal transport plans

By PulseAugur Editorial · [2 sources] ·

Researchers have established a theoretical link between Generative Flow Networks (GFlowNets) and optimal transport (OT). Their work demonstrates that non-acyclic GFlowNets, when optimized, can effectively encode an optimal transport plan. This connection allows the GFlowNet framework to be applied to large-scale OT problems using neural parameterization and edge flows, with experiments showing agreement with existing OT solvers. AI

IMPACT Establishes a new theoretical framework for applying GFlowNets to optimal transport problems, potentially improving efficiency in large-scale graph analysis.

RANK_REASON The cluster contains an academic paper detailing a theoretical connection between GFlowNets and optimal transport.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ian Maksimov, Nikita Morozov, Denis Belomestny, Sergey Samsonov ·

    Your GFlowNet Secretly Learns an Optimal Transport Plan

    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…

  2. arXiv cs.AI TIER_1 English(EN) · Sergey Samsonov ·

    Your GFlowNet Secretly Learns an Optimal Transport Plan

    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…

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