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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine-Learning the Sato--Tate Conjecture · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2010.01213/</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/2010.01213/feed.xml" rel="self" type="application/rss+xml" /><item><title>Machine-Learning the Sato--Tate Conjecture — mentioned by robertk</title><link>https://news.ycombinator.com/item?id=39609163</link><guid isPermaLink="false">urn:hackerpapers:mention:2010.01213:39609163</guid><pubDate>Tue, 05 Mar 2024 21:06:43 GMT</pubDate><description>&lt;p&gt;Very cool result but the title is overselling the &amp;quot;AI&amp;quot; contribution. It seems like they trained a few standard binary classifiers (Naive Bayes, decision trees, kNN). The novelty is the independent variable coming from an attribute precomputed for many known elliptic curves in the LMFDB database, namely the Dirichlet coefficients of the associated L-function; and the dependent variable being whether or not the elliptic curve has complex multiplication (CM), an important theoretical property for which lots of flashy theorems begin with assuming whether or not the curve has CM. They go on to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2010.01213/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>math.NT</category></item></channel></rss>
