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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Overcoming a Theoretical Limitation of Self-Attention · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2202.12172/</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/2202.12172/feed.xml" rel="self" type="application/rss+xml" /><item><title>Overcoming a Theoretical Limitation of Self-Attention — mentioned by shawntan</title><link>https://news.ycombinator.com/item?id=35614999</link><guid isPermaLink="false">urn:hackerpapers:mention:2202.12172:35614999</guid><pubDate>Tue, 18 Apr 2023 15:09:15 GMT</pubDate><description>&lt;p&gt;&amp;gt; I thought the key takeaway is that nonlinearity is important. Multilayer perceptrons can be collapsed into an equivalent single layer if there isn&amp;#x27;t nonlinearity thrown in. multi-input XOR can be solved using a form of sin(x) function after summing in a single layer (sin squared if you don&amp;#x27;t like negative numbers). Some element of this is used in this paper: https://arxiv.org/pdf/2202.12172.pdf More broadly, as I&amp;#x27;ve alluded to in other replies, XOR is not the problem in itself, but illustrates a bigger problem. Yes, non-linearities can solve problems, but one hidden layer and the wrong…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2202.12172/"&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>
