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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Adaptive Attention Span in Transformers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1905.07799/</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/1905.07799/feed.xml" rel="self" type="application/rss+xml" /><item><title>Adaptive Attention Span in Transformers — mentioned by voxgen</title><link>https://news.ycombinator.com/item?id=35280802</link><guid isPermaLink="false">urn:hackerpapers:mention:1905.07799:35280802</guid><pubDate>Thu, 23 Mar 2023 20:23:21 GMT</pubDate><description>&lt;p&gt;One can also make a model to learn the necessary context length for each layer and head to save a huge amount of FLOPs: https://arxiv.org/abs/1905.07799&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1905.07799/"&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>
