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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Online Learning with Gated Linear Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1712.01897/</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/1712.01897/feed.xml" rel="self" type="application/rss+xml" /><item><title>Online Learning with Gated Linear Networks — mentioned by T-A</title><link>https://news.ycombinator.com/item?id=23529492</link><guid isPermaLink="false">urn:hackerpapers:mention:1712.01897:23529492</guid><pubDate>Mon, 15 Jun 2020 16:49:41 GMT</pubDate><description>&lt;p&gt;Presumably (haven&amp;#x27;t read the paper yet) that their network provably becomes a universal function approximator in the limit of infinite size. Reading... actually the proof seems to be in https://arxiv.org/abs/1712.01897&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1712.01897/"&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>
