新理论解释浅层神经网络中的增量学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一个新的理论框架，用于理解浅层神经网络中的增量学习。这项工作侧重于在标准初始化下，在正交多指标目标上训练的多项式宽度两层网络。研究结果表明，增量学习仍然发生，损失根据目标的厄米展开式顺序下降，低阶分量先于高阶分量被学习。 AI

影响为神经网络训练动力学提供了理论见解，可能为未来的模型架构提供信息。

排序理由该条目是一篇详细介绍机器学习理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Mo Zhou, Weihang Xu, Simon S. Du, Maryam Fazel · 2026-09-11 04:00

学习超越小初始化时的正交多指标模型：增量学习、竞争动态与对称性

arXiv:2609.10879v1 Announce Type: cross Abstract: Recent work has identified incremental learning in shallow networks trained on single-index and multi-index models. However, existing analyses often rely on simplifying settings, such as small initialization, correlation loss, or …