English(EN) Mixtures of Neural Operators Reduce Active Complexity in Operator Learning

神经算子混合模型提升算子学习效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-10 04:00

研究人员开发了一种名为神经算子混合模型（MoNOs）的新方法，以提高算子学习系统的效率。该方法将输入函数路由到特定的“专家”神经算子，从而降低每次查询的计算复杂度。研究表明，与传统的单一算子结构相比，MoNOs可以用更小的活跃专家规模来近似算子，并具有近似精度的理论保证。 AI

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arXiv stat.ML TIER_1 English(EN) · Anastasis Kratsios, Takashi Furuya, Jose Antonio Lara Benitez, Matti Lassas, Maarten de Hoop · 2026-06-10 04:00

神经网络算子混合物降低算子学习中的活跃复杂度

arXiv:2404.09101v3 Announce Type: replace-cross Abstract: Operator-learning systems are not governed solely by total parameter count; for one query, the relevant bottleneck can be the model that must be loaded and evaluated. We study this distinction for classical neural operator…