English(EN) Learning the identity: a case study of how SGD selects among functional decompositions

研究论文分析SGD在学习同一性函数时选择解决方案

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

一篇新研究论文探讨了随机梯度下降（SGD）在深度线性残差网络中学习同一性函数时如何选择特定解决方案。尽管存在许多可最小化总体损失的解决方案，但SGD始终偏好特定的解决方案，这可以通过熵损失的视角来理解。这个熵项会惩罚小批量梯度期望的平方范数，有助于区分网络层之间同一性的不同功能分解。该研究分析性地描述了这种熵损失的最小化器，并利用这些预测来解释SGD训练网络的观察行为。 AI

影响为深度学习模型的优化动态提供了理论见解。

排序理由关于特定机器学习优化技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Andy Arditi, Weian Xie, David Bau, Liu Ziyin · 2026-10-03 04:00

学习身份：SGD在功能分解中进行选择的案例研究

arXiv:2610.00615v1 Announce Type: new Abstract: One might think that learning the identity function with a deep linear residual network is trivial - the path along residual connections already implements the identity, and so the network need only drive its weights to zero. Howeve…

本文如何被排名

报道来源 [1]

学习身份：SGD在功能分解中进行选择的案例研究

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