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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Controlled Differential Equations for Irregular Time Series · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2005.08926/</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/2005.08926/feed.xml" rel="self" type="application/rss+xml" /><item><title>Neural Controlled Differential Equations for Irregular Time Series — mentioned by BadInformatics</title><link>https://news.ycombinator.com/item?id=23595275</link><guid isPermaLink="false">urn:hackerpapers:mention:2005.08926:23595275</guid><pubDate>Sun, 21 Jun 2020 20:46:36 GMT</pubDate><description>&lt;p&gt;Was prepared for an introduction to some novel never-before-seen method near the end, but this was a nice and well-structured summary regardless! Wanted to tack on a couple of questions/thoughts: 1. Is the author familiar with all of the recent work around neural differential equations? Latent (Neural) ODEs[1] and Neural Controlled Differential Equations[2] are already able to match or exceed the performance of GRU-D when working with certain sparse and irregularly-sampled time series. 2. More of a nitpick, but most of the models covered as working with EHR data are using clinical data…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2005.08926/"&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>
