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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Saddle-free Hessian-free Optimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1506.00059/</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/1506.00059/feed.xml" rel="self" type="application/rss+xml" /><item><title>Saddle-free Hessian-free Optimization — mentioned by jph00</title><link>https://news.ycombinator.com/item?id=14282507</link><guid isPermaLink="false">urn:hackerpapers:mention:1506.00059:14282507</guid><pubDate>Sat, 06 May 2017 22:04:18 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s not so much that there&amp;#x27;s just one or two specific results, but rather that there&amp;#x27;s just far more researchers working on this now, and quite a few are working on more theoretical stuff. Sometimes, that theory results in really practical outcomes - a good example would be https://arxiv.org/abs/1701.07875 . Or another by the same researcher: https://arxiv.org/abs/1506.00059 . We&amp;#x27;ve seen, in the last year or two, interesting results in nearly every area of theoretical research of deep learning, including generalization, optimization, generative modelling, bayesian models, and network…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1506.00059/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>math.NA</category></item></channel></rss>
