English(EN) Stability of Transformers under Layer Normalization

新研究分析 Transformer 在层归一化下的稳定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-10 04:00

Kelvin Kan 的一篇新论文探讨了深度 Transformer 在训练过程中的稳定性，重点关注层归一化的放置位置。该研究为不同的放置如何影响隐藏状态的增长和梯度的反向传播提供了理论见解。该分析为优化 Transformer 架构和扩展残差步长以提高稳定性和性能提供了指导。 AI

影响为提高 Transformer 模型的稳定性和性能提供了理论指导。

排序理由在 arXiv 上发表的学术论文，详细介绍了模型架构的理论分析。[lever_c_demoted from research: ic=1 ai=1.0]

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: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 在 arXiv 上发表的学术论文，详细介绍了模型架构的理论分析。[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, infra; 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: 77 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

arXiv cs.AI TIER_1 English(EN) · Kelvin Kan, Xingjian Li, Benjamin J. Zhang, Tuhin Sahai, Stanley Osher, Krishna Kumar, Markos A. Katsoulakis · 2026-08-10 04:00

Transformer 在层归一化下的稳定性

arXiv:2510.09904v2 Announce Type: replace-cross Abstract: Despite their widespread use, training deep Transformers can be unstable. Layer normalization, a standard component, improves training stability, but its placement has often been ad-hoc. In this paper, we conduct a princip…