研究人员在监督学习场景中，为使用梯度流训练的专家混合（MoE）模型建立了一个均场极限。他们的研究结果表明，随着专家数量的增加，模型的参数会收敛到一个满足非线性连续性方程的概率测度。这种收敛速度明确地取决于专家的数量，并且该研究将这些结果应用于由量子神经网络生成的MoE模型。 AI
影响 对理解MoE收敛性的理论进展，可能影响未来的模型架构。
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研究人员通过专家混合模型探索量子神经网络
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从通用专家混合模型到量子神经网络的均场极限
arXiv:2501.14660v2 Announce Type: replace-cross Abstract: In this work, we study the asymptotic behavior of Mixture of Experts (MoE) trained via gradient flow on supervised learning problems. Our main result establishes the propagation of chaos for a MoE as the number of experts …