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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>An introduction to Differential Linear Logic: proof-nets, models and antiderivatives · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1606.01642/</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/1606.01642/feed.xml" rel="self" type="application/rss+xml" /><item><title>An introduction to Differential Linear Logic: proof-nets, models and antiderivatives — mentioned by bmc7505</title><link>https://news.ycombinator.com/item?id=27019880</link><guid isPermaLink="false">urn:hackerpapers:mention:1606.01642:27019880</guid><pubDate>Sun, 02 May 2021 23:02:29 GMT</pubDate><description>&lt;p&gt;Surprisingly, there is an interesting proof-theoretic connection between differential and sequent calculus! For a more detailed account of differential linear logic (DiLL), see Erhard [1] and Kerjean [2]. More generally, the same rules from differential calculus have reappeared in strange and marvelous places throughout computer science, including formal language theory [3], parsing [4], type theory [5] and automata theory [6]. We do not attempt to cover all these topics, research is just beginning to understand how they are related. The general theme seems to be, how do you propagate local…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1606.01642/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LO</category></item><item><title>An introduction to Differential Linear Logic: proof-nets, models and antiderivatives — mentioned by noelwelsh</title><link>https://news.ycombinator.com/item?id=16985121</link><guid isPermaLink="false">urn:hackerpapers:mention:1606.01642:16985121</guid><pubDate>Thu, 03 May 2018 11:59:33 GMT</pubDate><description>&lt;p&gt;For a loop, you can unroll it and define the derivative that way. But you&amp;#x27;re correct in general that there are programs that have no sensible derivative but automatic differentiation will still compute something. Going beyond AD there is a &amp;quot;differential lambda calculus&amp;quot; [1]. I don&amp;#x27;t really understand this work. It requires more background in programming languages, logic, and analysis than I have time to learn. My understanding is it allows you to compute more derivatives (e.g. for higher-order functions) and place conditions so only sensible derivatives are computed. There is also…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1606.01642/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LO</category></item></channel></rss>
