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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Understanding the Double Descent Phenomenon in Deep Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2403.10459/</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/2403.10459/feed.xml" rel="self" type="application/rss+xml" /><item><title>Understanding the Double Descent Phenomenon in Deep Learning — mentioned by rhdunn</title><link>https://news.ycombinator.com/item?id=50301908</link><guid isPermaLink="false">urn:hackerpapers:mention:2403.10459:50301908</guid><pubDate>Fri, 30 Oct 2026 10:13:50 GMT</pubDate><description>&lt;p&gt;AFAICT there are several things that affect the size of the model: 1. the number of transformer layers -- e.g. each layer adds the ability to provide more information to the residual stream [1], [2]; each layer is `tokens * features * active context` 2. the number of tokens -- the more tokens there are the more word fragments and Unicode characters you can represent 3. the number of features assigned for a token -- these are used to associate trained semantic information for the token; with the active context this can encode contextual information for that token (the idea being that the model…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2403.10459/"&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>
