<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>You Only Linearize Once: Tangents Transpose to Gradients · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2204.10923/</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/2204.10923/feed.xml" rel="self" type="application/rss+xml" /><item><title>You Only Linearize Once: Tangents Transpose to Gradients — mentioned by mattjjatgoogle</title><link>https://news.ycombinator.com/item?id=34757552</link><guid isPermaLink="false">urn:hackerpapers:mention:2204.10923:34757552</guid><pubDate>Sat, 11 Feb 2023 23:27:12 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;m sure there&amp;#x27;s a lot of good material around, but here are some links that are conceptually very close to the linked Autodidax. (Disclaimer: I wrote Autodidax and some of these other materials.) There&amp;#x27;s Autodidact [0], a predecessor to Autodidax, which was a simplified implementation of the original Autograd [1]. It focuses on reverse-mode autodiff, not building an open-ended transformation system like Autodidax. It&amp;#x27;s also pretty close to the content in these lecture slides [2] and this talk [3]. But the autodiff in Autodidax is more sophisticated and reflects clearer thinking. In…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2204.10923/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.PL</category></item></channel></rss>
