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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Automatic Differentiation: a look through Tensor and Operational Calculus · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1612.02731/</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/1612.02731/feed.xml" rel="self" type="application/rss+xml" /><item><title>Automatic Differentiation: a look through Tensor and Operational Calculus — mentioned by m_hack</title><link>https://news.ycombinator.com/item?id=13284280</link><guid isPermaLink="false">urn:hackerpapers:mention:1612.02731:13284280</guid><pubDate>Fri, 30 Dec 2016 13:40:29 GMT</pubDate><description>&lt;p&gt;I spent the last few hours reading this paper. From what I understand, the paper is not about AD, but something more. AD is &amp;quot;efficient means to calculating derivatives&amp;quot;, as they put it. This paper seems to be about program analysis through calculus (AD comes into play, as derivatives are needed). They show how to form equations with programs and calculate with them, kind of like with &amp;quot;normal&amp;quot; math. This seems to be the reason for constructing the algebra. They derive some transformation of programs similar to Fouriers I do not fully understand yet (they also use this algebra and calculus to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1612.02731/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.SC</category></item></channel></rss>
