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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gradient Descent Finds Global Minima of Deep Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1811.03804/</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/1811.03804/feed.xml" rel="self" type="application/rss+xml" /><item><title>Gradient Descent Finds Global Minima of Deep Neural Networks — mentioned by mlthoughts2018</title><link>https://news.ycombinator.com/item?id=18637796</link><guid isPermaLink="false">urn:hackerpapers:mention:1811.03804:18637796</guid><pubDate>Sat, 08 Dec 2018 22:03:07 GMT</pubDate><description>&lt;p&gt;I suspect actually there is some connection between optimal batch size and the proofs of this paper regarding how deep NN will provably converge to zero training loss with gradient descent (not stochastic gradient descent). https://arxiv.org/abs/1811.03804&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1811.03804/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Gradient Descent Finds Global Minima of Deep Neural Networks — HN submission</title><link>https://news.ycombinator.com/item?id=18433058</link><guid isPermaLink="false">urn:hackerpapers:submission:1811.03804:18433058</guid><pubDate>Mon, 12 Nov 2018 15:17:58 GMT</pubDate><description>&lt;p&gt;Gradient Descent Finds Global Minima of Deep Neural Networks&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;133 points; 57 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1811.03804/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
