<?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>Weight-Sharing on 3rd layer</title><link>https://3rdlayer.uk/tags/weight-sharing/</link><description>Recent content in Weight-Sharing 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/weight-sharing/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></channel></rss>