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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Toward generalizable learning of all (linear) first-order methods via memory augmented Transformers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2410.07263/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:27:26 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2410.07263/feed.xml" rel="self" type="application/rss+xml" /><item><title>Toward generalizable learning of all (linear) first-order methods via memory augmented Transformers — HN submission</title><link>https://news.ycombinator.com/item?id=41973952</link><guid isPermaLink="false">urn:hackerpapers:submission:2410.07263:41973952</guid><pubDate>Mon, 28 Oct 2024 17:48:17 GMT</pubDate><description>&lt;p&gt;Memory-augmented Transformers can implement Linear first-Order Optimization&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2410.07263/"&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>
