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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Categorical Foundations of Gradient-Based Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2103.01931/</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/2103.01931/feed.xml" rel="self" type="application/rss+xml" /><item><title>Categorical Foundations of Gradient-Based Learning — mentioned by bts</title><link>https://news.ycombinator.com/item?id=43839427</link><guid isPermaLink="false">urn:hackerpapers:mention:2103.01931:43839427</guid><pubDate>Tue, 29 Apr 2025 23:45:20 GMT</pubDate><description>&lt;p&gt;Indeed they call for new names, as they encompass far more than iterators. If you read a bit more about them, I think you will be surprised to see the breadth of what these abstractions can be used for. To start, they&amp;#x27;ve been used to build a new compositional foundation for game theory[1], and they can be used to model gradient-based learning in machine learning[2]. As for their simpler applications as getters and setters, they are programmable in the sense that you can think of lens/prism types as interfaces that can be implemented arbitrarily. So you can create your own lenses and combine…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2103.01931/"&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>
