English(EN) From Approximation Rates to Loss-Landscape Barrier Decay in Shallow ReLU Networks

新理论探讨浅层ReLU网络中的损失景观障碍衰减

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-14 04:00

研究人员开发了一个新的理论框架，用于理解具有ReLU激活函数的浅层神经网络的损失景观。该研究引入了一种分析子集路径连通性的方法，从而深入了解网络在训练过程中的损失函数行为。这项工作为近似误差和障碍衰减率提供了理论界限，并通过使用Huber损失函数的实验验证得到了补充。 AI

影响为ReLU网络的训练动态提供了理论见解，可能为未来的模型优化技术提供信息。

排序理由关于神经网络损失景观理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Saveliy Baturin · 2026-08-14 04:00

从近似率到浅层ReLU网络中的损失景观障碍衰减

arXiv:2602.17596v2 Announce Type: replace Abstract: We study pathwise connectivity of sublevel sets for one-hidden-layer ReLU networks with constrained first-layer weights and an $\ell_1$ penalty on the output layer. The data term is assumed convex and globally Lipschitz in the s…