<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dynamics of Deep Neural Networks and Neural Tangent Hierarchy · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1909.08156/</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/1909.08156/feed.xml" rel="self" type="application/rss+xml" /><item><title>Dynamics of Deep Neural Networks and Neural Tangent Hierarchy — mentioned by fgabriel</title><link>https://news.ycombinator.com/item?id=21655064</link><guid isPermaLink="false">urn:hackerpapers:mention:1909.08156:21655064</guid><pubDate>Thu, 28 Nov 2019 06:30:28 GMT</pubDate><description>&lt;p&gt;For trained wide neural networks, you can have a look at &amp;quot;Neural Tangent Kernel: Convergence and Generalization in Neural Networks&amp;quot; (https://arxiv.org/abs/1806.07572) where we explain the training of very wide ANN. This sparked a numerous amount of research and in the few last results about training wide ANN you can have a look at: https://arxiv.org/abs/1909.08156 and https://arxiv.org/abs/1904.11955&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1909.08156/"&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>
