English(EN) On Compatible Generalization Bounds of Deep Residual Networks From a Dynamical System Perspective

新的深度残差网络界限被分析为动力学系统

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

研究人员通过将深度残差网络（ResNets）分析为动力学系统，为其开发了新的兼容泛化界。这项工作侧重于深度层极限下具有广泛激活函数的ResNets。研究结果为离散时间和连续时间ResNets建立了统一的泛化界，其样本率达到了最优的O(1/√S)，并且首项与层数无关。 AI

影响为深度残差网络建立了理论界限，可能提高深度学习模型的理解和性能。

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arXiv cs.LG TIER_1 English(EN) · Jinshu Huang, Mingfei Sun, Chunlin Wu · 2026-11-11 05:00

从动力学系统视角看深度残差网络的兼容泛化界限

arXiv:2602.20921v2 Announce Type: replace Abstract: Deep neural networks (DNNs) have significantly advanced machine learning, with model depth playing a central role in their successes. Deep residual networks (ResNets) fall into one of the most important classes of DNNs. In this …