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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>An Attention Free Transformer · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2105.14103/</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/2105.14103/feed.xml" rel="self" type="application/rss+xml" /><item><title>An Attention Free Transformer — mentioned by nl</title><link>https://news.ycombinator.com/item?id=34447530</link><guid isPermaLink="false">urn:hackerpapers:mention:2105.14103:34447530</guid><pubDate>Fri, 20 Jan 2023 00:16:15 GMT</pubDate><description>&lt;p&gt;This isn&amp;#x27;t true - the model architecture matters a lot. In general RNNs cannot handle long term dependencies (ie, long pieces of text) because the gradient vanishes. It&amp;#x27;s unclear how this solves this problem although they do reference the &amp;quot;attention free transformer&amp;quot; paper: https://arxiv.org/abs/2105.14103&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2105.14103/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>An Attention Free Transformer — mentioned by nl</title><link>https://news.ycombinator.com/item?id=34446689</link><guid isPermaLink="false">urn:hackerpapers:mention:2105.14103:34446689</guid><pubDate>Thu, 19 Jan 2023 22:40:35 GMT</pubDate><description>&lt;p&gt;For those wondering how on earth they are getting decent results from a RNN without long range forgetting, I don&amp;#x27;t really know either! But they reference https://arxiv.org/abs/2105.14103 and the bottom section of https://github.com/BlinkDL/RWKV-LM has an explainer.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2105.14103/"&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>
