<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Xavier - 标签 - mywebsite</title><link>https://steven-yl.github.io/mywebsite/tags/xavier/</link><description>Xavier - 标签 - mywebsite</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>steven@gmail.com (Steven)</managingEditor><webMaster>steven@gmail.com (Steven)</webMaster><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Fri, 03 Apr 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://steven-yl.github.io/mywebsite/tags/xavier/" rel="self" type="application/rss+xml"/><item><title>Pytorch 权重初始化方法</title><link>https://steven-yl.github.io/mywebsite/net_init/</link><pubDate>Thu, 12 Mar 2026 00:00:00 +0800</pubDate><author><name>Steven</name><uri>https://github.com/steven-yl</uri></author><guid>https://steven-yl.github.io/mywebsite/net_init/</guid><description>全面对比深度学习权重初始化方法的原理、公式推导、优缺点与适用场景，附 PyTorch 代码示例和 Transformer 架构初始化最佳实践。</description></item><item><title>Kaiming（He）初始化：方差推导与 ReLU 网络</title><link>https://steven-yl.github.io/mywebsite/kaiming/</link><pubDate>Fri, 03 Apr 2026 00:00:00 +0800</pubDate><author><name>Steven</name><uri>https://github.com/steven-yl</uri></author><guid>https://steven-yl.github.io/mywebsite/kaiming/</guid><description>用前向方差分析解释为何 ReLU 网络宜用方差 2/fan_in 的权重初始化，并对比 Xavier、给出 PyTorch 中的对应实现。</description></item></channel></rss>