English(EN) Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study

新理论解释算子学习中的神经缩放定律

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-27 04:00

本文提出了一个理论框架，用于理解深度算子网络（特别是DeepONet等架构）中的神经缩放定律。该研究分析了与网络大小和训练数据相关的逼近误差和泛化误差，为具有低维结构的输入提供了更严格的界限。这些发现也延伸到深度ReLU网络，为算子学习提供了理论基础。 AI

排序理由这是一项在arXiv上发表的关于机器学习中神经缩放定律的理论研究。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Hao Liu, Zecheng Zhang, Wenjing Liao, Hayden Schaeffer · 2026-05-27 04:00

深度ReLU和深度算子网络（DeepONet）的神经缩放定律：一项理论研究

arXiv:2410.00357v2 Announce Type: replace-cross Abstract: Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains und…