<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Foundation Model on Text Matrix</title><link>https://txtmix.com/tags/foundation-model/</link><description>Recent content in Foundation Model on Text Matrix</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sat, 23 May 2026 08:55:34 +0800</lastBuildDate><atom:link href="https://txtmix.com/tags/foundation-model/index.xml" rel="self" type="application/rss+xml"/><item><title>TabPFN: 表格数据的 Foundation Model 完整指南</title><link>https://txtmix.com/posts/tech/tabpfn-tabular-foundation-model-guide/</link><pubDate>Wed, 06 May 2026 10:07:31 +0800</pubDate><guid>https://txtmix.com/posts/tech/tabpfn-tabular-foundation-model-guide/</guid><description>&lt;h1 id="tabpfn-表格数据的-foundation-model-完整指南">TabPFN: 表格数据的 Foundation Model 完整指南&lt;/h1>
&lt;p>机器学习实践中，表格数据是最常见也最顽固的领域之一。长期以来，处理表格数据的标准流程是：选模型、调超参、反复训练——这一套下来，少则几十分钟，多则几天。面对一个陌生数据集，光是跑通一个 Baseline，就可能耗掉工程师大半天时间。&lt;/p></description></item></channel></rss>