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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Universal Neural Functionals · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2402.05232/</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/2402.05232/feed.xml" rel="self" type="application/rss+xml" /><item><title>Universal Neural Functionals — mentioned by tudorw</title><link>https://news.ycombinator.com/item?id=39418177</link><guid isPermaLink="false">urn:hackerpapers:mention:2402.05232:39418177</guid><pubDate>Sun, 18 Feb 2024 11:22:08 GMT</pubDate><description>&lt;p&gt;Maybe! I&amp;#x27;m lost in the maths, &amp;quot;Our results suggest that learned optimizers can benefit from considering the (symmetry) structure of the weight space they optimize. &amp;quot; this from 7th Feb came out of DeepMind; https://arxiv.org/abs/2402.05232 When it comes to the math underneath an LLM, https://medium.com/autonomous-agents/part-8-mathematical-exp... is about the most accessible explanation I have found so far.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2402.05232/"&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>
