<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>RMSNorm on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/rmsnorm/</link><description>Recent content in RMSNorm on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Thu, 02 Jul 2026 11:02:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/rmsnorm/index.xml" rel="self" type="application/rss+xml"/><item><title>神经网络归一化：LN vs BN vs RMSNorm</title><link>https://guijiagi.com/posts/neural-network-normalization/</link><pubDate>Thu, 02 Jul 2026 11:02:00 +0800</pubDate><guid>https://guijiagi.com/posts/neural-network-normalization/</guid><description>深度解析Layer Normalization、RMSNorm等归一化技术的原理、差异与演进</description></item><item><title>LayerNorm vs RMSNorm：Transformer 归一化的选择</title><link>https://guijiagi.com/posts/layer-normalization-deep/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/layer-normalization-deep/</guid><description>深入对比 LayerNorm 与 RMSNorm 的原理、数值稳定性与性能差异</description></item></channel></rss>