English(EN) Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks

新研究详细介绍了神经网络损失景观中的局部极小值

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-01 04:00

研究人员详细介绍了两层ReLU神经网络的损失景观，揭示了局部极小值可以通过汇总统计量进行精确表征。这项工作建立了这些极小值与单遍随机梯度下降（SGD）动力学之间的联系，表明极小值存在分层组织。研究表明，在过参数化的情况下，全局极小值变得更容易获得，从而减少了对错误解的收敛。 AI

影响提供了对神经网络训练动力学的更深层次的理论理解，可能为未来的优化技术提供信息。

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arXiv stat.ML TIER_1 English(EN) · Jie Huang, Bruno Loureiro, Stefano Sarao Mannelli · 2026-06-01 04:00

高维两层ReLU神经网络损失景观中局部极小的尖锐描述

arXiv:2604.09412v2 Announce Type: replace Abstract: We study the population loss landscape of two-layer ReLU networks of the form $\sum_{k=1}^K \mathrm{ReLU}(w_k^\top x)$ in a realisable teacher-student setting with Gaussian covariates. We show that local minima admit an exact lo…