<?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>Representation-Learning on Yuchen Du</title><link>https://dudusoar.github.io/tags/representation-learning/</link><description>Recent content in Representation-Learning on Yuchen Du</description><generator>Hugo -- 0.164.0</generator><language>en-us</language><lastBuildDate>Mon, 08 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://dudusoar.github.io/tags/representation-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>A Generative Model Is Like a Database: Decomposition Defines Its Schema</title><link>https://dudusoar.github.io/posts/generative-models-database/</link><pubDate>Mon, 08 Jun 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/generative-models-database/</guid><description>A database analogy reveals why generative models need data decomposition and why unit granularity determines what they can learn and generate.</description></item></channel></rss>