<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2001.06931/</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/2001.06931/feed.xml" rel="self" type="application/rss+xml" /><item><title>Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective — mentioned by benibraz</title><link>https://news.ycombinator.com/item?id=24141311</link><guid isPermaLink="false">urn:hackerpapers:mention:2001.06931:24141311</guid><pubDate>Thu, 13 Aug 2020 09:01:47 GMT</pubDate><description>&lt;p&gt;What do you mean by &amp;#x27;the best&amp;#x27;? Deeper architectures are popular because they quiet easy to train. They do work well in practice on many tasks (especially vision) but they have their limits. Infinite wide networks are a newly active field and has recently shown some promising results, theoretically [1, 2] and empirically [3]. [1] https://arxiv.org/abs/2001.06931 [3] https://arxiv.org/abs/1806.07572 [2] https://ai.googleblog.com/2020/03/fast-and-easy-infinitely-w...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2001.06931/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
