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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Block-Recurrent Transformers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2203.07852/</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/2203.07852/feed.xml" rel="self" type="application/rss+xml" /><item><title>Block-Recurrent Transformers — mentioned by albertzeyer</title><link>https://news.ycombinator.com/item?id=35684394</link><guid isPermaLink="false">urn:hackerpapers:mention:2203.07852:35684394</guid><pubDate>Mon, 24 Apr 2023 06:47:52 GMT</pubDate><description>&lt;p&gt;This looks like a potential NeurIPS submission. But it will probably be rejected. The quality bar for NeurIPS is quite high. Some reasons: The experiments are very weak: There are just a few figures, basically figure 1 and figure 5, which show some results. There are no tables with numbers. But more importantly: There are no comparisons (in terms of experiments/numbers) to similar models, like: - Block-Recurrent Transformers (https://arxiv.org/abs/2203.07852) and related approaches to make the Transformer recurrent, so effectively getting infinite context length. - All the work on sparse…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2203.07852/"&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>
