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English(EN) Trajectory balance: Improved credit assignment in GFlowNets

新的“轨迹平衡”目标改进了 GFlowNet 学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了“轨迹平衡”，这是生成流网络 (GFlowNets) 的一种新颖学习目标。这个新目标旨在改进 GFlowNets 中的信用分配，GFlowNets 用于从非归一化密度生成组合对象，如图和字符串。所提出的方法被认为是现有目标（如流匹配和详细平衡）的更有效替代方案，这些目标在处理长动作序列时可能会遇到困难。在四个领域的实验证明了轨迹平衡在提高 GFlowNet 收敛性、样本多样性和鲁棒性方面的有效性。 AI

影响 引入了一种更有效的方法来训练生成模型，有可能提高它们创建多样化和复杂数据的能力。

排序理由 详细介绍生成模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的“轨迹平衡”目标改进了 GFlowNet 学习

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详细介绍生成模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio, Chen Sun, Yoshua Bengio ·

    轨迹平衡：GFlowNets 中改进的信用分配

    arXiv:2201.13259v4 Announce Type: replace-cross Abstract: Generative flow networks (GFlowNets) are a method for learning a stochastic policy for generating compositional objects, such as graphs or strings, from a given unnormalized density by sequences of actions, where many poss…

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