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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2408.13233/</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/2408.13233/feed.xml" rel="self" type="application/rss+xml" /><item><title>Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time — mentioned by janwas</title><link>https://news.ycombinator.com/item?id=41473535</link><guid isPermaLink="false">urn:hackerpapers:mention:2408.13233:41473535</guid><pubDate>Sat, 07 Sep 2024 12:26:41 GMT</pubDate><description>&lt;p&gt;+1. Personal opinion: accelerators are useful today but have kept us in a local minimum which is certainly not the ideal. There are interesting approaches such as near linear low-rank approximation of attention gradients [1]. Would we rather have that, or somewhat better constant factors? [1] https://arxiv.org/html/2408.13233v1&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2408.13233/"&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>
