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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fast inference of Boosted Decision Trees in FPGAs for particle physics · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2002.02534/</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/2002.02534/feed.xml" rel="self" type="application/rss+xml" /><item><title>Fast inference of Boosted Decision Trees in FPGAs for particle physics — mentioned by magicalhippo</title><link>https://news.ycombinator.com/item?id=27599718</link><guid isPermaLink="false">urn:hackerpapers:mention:2002.02534:27599718</guid><pubDate>Wed, 23 Jun 2021 01:34:23 GMT</pubDate><description>&lt;p&gt;&amp;gt; I have yet to see a real world production pipeline where GBDT provides enough improvement over Logistic Regression Not my field at all, so &amp;quot;I know nooothing&amp;quot;. Are GBDT&amp;#x27;s very different from &amp;quot;plain&amp;quot; binary decision trees? I&amp;#x27;ve seen the latter a lot in the context of particle experiments[1][2][3]. [1]: https://arxiv.org/abs/physics/0408124 [2]: http://cds.cern.ch/record/2289251/ [3]: https://arxiv.org/abs/2002.02534&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2002.02534/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>physics.comp-ph</category></item></channel></rss>
