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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Local minima in training of neural networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.06310/</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/1611.06310/feed.xml" rel="self" type="application/rss+xml" /><item><title>Local minima in training of neural networks — mentioned by chillee</title><link>https://news.ycombinator.com/item?id=16012066</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.06310:16012066</guid><pubDate>Wed, 27 Dec 2017 00:57:43 GMT</pubDate><description>&lt;p&gt;That&amp;#x27;s why I said high dimensional neural networks. There&amp;#x27;s been a lot of literature explaining why local minima aren&amp;#x27;t a problem in very high dimension loss surfaces. Check any of the literature on this subject: https://arxiv.org/abs/1611.06310v2 https://arxiv.org/abs/1406.2572 Local minima are something that people thought was gonna be a problem, especially back in the 2000s. They played around with small neural nets on toy examples such as yours, and thought it was intractable. It&amp;#x27;s the entire reason why neural nets fell out of the fashion in the early 2000s, and people moved towards…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.06310/"&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>
