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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2104.03279/</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/2104.03279/feed.xml" rel="self" type="application/rss+xml" /><item><title>Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction — mentioned by ArtWomb</title><link>https://news.ycombinator.com/item?id=27010147</link><guid isPermaLink="false">urn:hackerpapers:mention:2104.03279:27010147</guid><pubDate>Sat, 01 May 2021 21:43:23 GMT</pubDate><description>&lt;p&gt;Trending as John Hopfield scheduled to present his &amp;quot;biologically plausible&amp;quot; response to the Modern Hopfield Network at ICLR next week: Large Associative Memory Problem in Neurobiology and Machine Learning https://arxiv.org/abs/2008.06996 MHN seem ideal for prediction problems based purely on data, such as chemical reactions and drug discovery: Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction https://arxiv.org/abs/2104.03279&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2104.03279/"&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>
