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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gated Linear Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1910.01526/</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/1910.01526/feed.xml" rel="self" type="application/rss+xml" /><item><title>Gated Linear Networks — mentioned by vintermann</title><link>https://news.ycombinator.com/item?id=35319658</link><guid isPermaLink="false">urn:hackerpapers:mention:1910.01526:35319658</guid><pubDate>Sun, 26 Mar 2023 21:09:07 GMT</pubDate><description>&lt;p&gt;&amp;gt; no one is trying to do statistical inference using compression Well, maybe no one sensible? I still think it&amp;#x27;s quite cool that you can take any general purpose compression algorithm, and abuse it to do e.g. image classification. (Just append the image you want to classify to each of the classes&amp;#x27; sets in turn, and see which compresses best!) And actually I do remember a paper that tried to use ideas from PAQ to do online learning. Gated Linear Networks, out of DeepMind, in 2019: https://arxiv.org/abs/1910.01526 All compression is related to AI, but especially sample efficient online learning…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1910.01526/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Gated Linear Networks — HN submission</title><link>https://news.ycombinator.com/item?id=23528247</link><guid isPermaLink="false">urn:hackerpapers:submission:1910.01526:23528247</guid><pubDate>Mon, 15 Jun 2020 15:23:04 GMT</pubDate><description>&lt;p&gt;Gated Linear Networks&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;150 points; 29 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1910.01526/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
