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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gradient Kernel Regression · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2104.05874/</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/2104.05874/feed.xml" rel="self" type="application/rss+xml" /><item><title>Gradient Kernel Regression — mentioned by mvcalder</title><link>https://news.ycombinator.com/item?id=28840568</link><guid isPermaLink="false">urn:hackerpapers:mention:2104.05874:28840568</guid><pubDate>Tue, 12 Oct 2021 14:59:30 GMT</pubDate><description>&lt;p&gt;When I first read about the NTK I was motivated to try an experiment to see if the kernel depended on where in the parameter space one evaluated it. I was surprised to see in my simple example the answer was &amp;quot;no&amp;quot;. Here is a write up I did: https://arxiv.org/pdf/2104.05874.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2104.05874/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
