English(EN) Minimax rates for learning spectral Barron functions by deep ReLU neural networks

深度ReLU网络在谱Barron函数上实现Minimax最优学习率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员为深度神经网络学习谱Barron函数建立了新的Minimax率。研究表明，这些可以被浅层网络有效逼近的函数，也可以被深度ReLU网络逼近，其速率取决于非零参数的数量。此外，研究表明深度ReLU网络可以以快速收敛速率学习谱Barron函数，并被证明在对数因子范围内是Minimax最优的。 AI

影响为深度网络学习复杂函数建立了理论界限，可能为未来的模型架构提供信息。

排序理由详细介绍神经网络学习率理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 5 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍神经网络学习率理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Songqiu Ma, Yunfei Yang · 2026-10-01 04:00

深度ReLU神经网络学习谱Barron函数的最优Minimax率

arXiv:2609.39020v1 Announce Type: new Abstract: We study how well deep neural networks approximate and learn spectral Barron functions. Recent studies have shown that these function classes can be efficiently approximated by shallow neural networks without suffering from the curs…

本文如何被排名

报道来源 [1]

深度ReLU神经网络学习谱Barron函数的最优Minimax率

相关实体

相关话题