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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>On the saddle point problem for non-convex optimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1405.4604/</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/1405.4604/feed.xml" rel="self" type="application/rss+xml" /><item><title>On the saddle point problem for non-convex optimization — mentioned by shmel</title><link>https://news.ycombinator.com/item?id=10678006</link><guid isPermaLink="false">urn:hackerpapers:mention:1405.4604:10678006</guid><pubDate>Fri, 04 Dec 2015 18:20:25 GMT</pubDate><description>&lt;p&gt;Mainly because in high-dimensional space saddle points are much more common and many local minima have very close fitness. Look for example http://arxiv.org/abs/1405.4604 and following papers.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1405.4604/"&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>
