<?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>K-Means on 3rd layer</title><link>https://3rdlayer.uk/tags/k-means/</link><description>Recent content in K-Means on 3rd layer</description><generator>Hugo -- 0.154.5</generator><language>en-US</language><lastBuildDate>Sat, 06 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://3rdlayer.uk/tags/k-means/index.xml" rel="self" type="application/rss+xml"/><item><title>Shrinking Models by Sharing Weights — K-Means-based Quantization</title><link>https://3rdlayer.uk/posts/kmeans-weight-quantization/</link><pubDate>Sat, 06 Jun 2026 00:00:00 +0000</pubDate><guid>https://3rdlayer.uk/posts/kmeans-weight-quantization/</guid><description>Group a neural network&amp;rsquo;s weights into a few representative values with K-Means, and you can shrink the model several-fold with almost no accuracy loss. We explore it with an interactive widget where you watch the clusters converge and the storage shrink in real time. (Deep Compression, Han et al. 2016)</description></item><item><title>K-means, GMM, EM: Three Nested Russian Dolls of Clustering</title><link>https://3rdlayer.uk/posts/em-kmeans-gmm/</link><pubDate>Sun, 10 Dec 2023 00:00:00 +0000</pubDate><guid>https://3rdlayer.uk/posts/em-kmeans-gmm/</guid><description>K-means is actually an extreme case of GMM, and GMM is the canonical application of the EM algorithm. How these three connect within a single framework, and how information geometry explains the relationship.</description></item></channel></rss>