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新的MMFLD方法优化约束域上的概率测度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了Mirror Mean-Field Langevin Dynamics (MMFLD)来解决概率测度约束域上的优化问题。这种新方法扩展了现有的均值场算法，而这些算法通常仅限于无约束空间。MMFLD专为优化\(\mathbb{R}^d\)的凸子集内的概率测度而设计，为复杂相互作用粒子系统（如无限宽度神经网络中的系统）提供了解决方案。 AI

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排序理由 该集群包含一篇详细介绍新型优化问题算法的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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新的MMFLD方法优化约束域上的概率测度

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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Anming Gu, Juno Kim ·

    Mirror Mean-Field Langevin Dynamics

    arXiv:2505.02621v2 Announce Type: replace-cross Abstract: The mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional on the Wasserstein space over $\mathbb{R}^d$, and has gained attention recently as a model for the gradient descent dynam…

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