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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Towards Understanding Mixture of Experts in Deep Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2208.02813/</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/2208.02813/feed.xml" rel="self" type="application/rss+xml" /><item><title>Towards Understanding Mixture of Experts in Deep Learning — mentioned by magicalhippo</title><link>https://news.ycombinator.com/item?id=40686483</link><guid isPermaLink="false">urn:hackerpapers:mention:2208.02813:40686483</guid><pubDate>Sat, 15 Jun 2024 00:27:58 GMT</pubDate><description>&lt;p&gt;From what I can gather it depends, but could be a simple Softmax-based layer[1] or just argmax[2]. There was also a recent post[3] about a model where they used a cross-attention layer to let the expert selection be more context aware. [1]: https://arxiv.org/abs/1701.06538 [2]: https://arxiv.org/abs/2208.02813 [3]: https://news.ycombinator.com/item?id=40675577&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2208.02813/"&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>Towards Understanding Mixture of Experts in Deep Learning — mentioned by jxf</title><link>https://news.ycombinator.com/item?id=37077473</link><guid isPermaLink="false">urn:hackerpapers:mention:2208.02813:37077473</guid><pubDate>Thu, 10 Aug 2023 15:31:02 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s not quite the same thing, but the &amp;quot;mixture of experts&amp;quot; model is a popular deep learning approach that displays very good performance: see e.g., https://arxiv.org/abs/2208.02813&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2208.02813/"&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>
