Muon框架为深度学习提供新的谱Wasserstein距离

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-11 04:00

研究人员引入了一个名为Muon的新框架，通过谱归一化来稳定深度学习优化，特别是针对矩阵形状的参数。这项工作在均值场条件下理想化了连续时间、动量衰减的训练动力学，将宽模型表示为参数空间上的概率测度。该研究定义了谱Wasserstein距离，并开发了静态Kantorovich和Benamou--Brenier公式，为归一化训练动力学提供了梯度流解释。 AI

影响引入了一个新颖的数学框架来稳定深度学习优化，可能改善宽模型的训练动力学。

排序理由该集群包含一篇学术论文，详细介绍了用于深度学习优化的新数学框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Gabriel Peyr\'e · 2026-05-11 04:00

Muon Dynamics as a Spectral Wasserstein Flow

arXiv:2604.04891v2 Announce Type: replace-cross Abstract: Gradient normalization stabilizes deep-learning optimization, and spectral normalizations are especially natural for matrix-shaped parameter blocks; Muon is the motivating example. We study an idealized deterministic, cont…

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Muon Dynamics as a Spectral Wasserstein Flow

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