<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Discovering Structure in High-Dimensional Data Through Correlation Explanation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1406.1222/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1406.1222/feed.xml" rel="self" type="application/rss+xml" /><item><title>Discovering Structure in High-Dimensional Data Through Correlation Explanation — mentioned by eli_gottlieb</title><link>https://news.ycombinator.com/item?id=10676925</link><guid isPermaLink="false">urn:hackerpapers:mention:1406.1222:10676925</guid><pubDate>Fri, 04 Dec 2015 15:47:58 GMT</pubDate><description>&lt;p&gt;Actually, there&amp;#x27;s been a few papers showing solid theoretical progress on understanding what structure in data deep networks are learning to represent. https://github.com/gregversteeg/CorEx http://arxiv.org/abs/1406.1222 http://arxiv.org/abs/1410.7404 Disclaimer: I reinvented the idea about a month or two ago, and ran into the existing papers when googling for joint entropy estimators after making some interesting graphs with hierarchical probabilistic programs. I only wish I could&amp;#x27;ve been the first!&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1406.1222/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
