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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Relaxation: partial differential equations for optimizing deep neural networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1704.04932/</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/1704.04932/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Relaxation: partial differential equations for optimizing deep neural networks — mentioned by phonebucket</title><link>https://news.ycombinator.com/item?id=23871458</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.04932:23871458</guid><pubDate>Fri, 17 Jul 2020 15:19:11 GMT</pubDate><description>&lt;p&gt;&amp;gt; it seems to me that even the experts do little real math and lots of guesswork Machine Learning is a rich and varied field. Like most applied sciences, there is a spectrum from the heavily applied to the heavily theoretical, to some which try to span both sides of the spectrum at the same time (e.g. https://arxiv.org/abs/1704.04932).&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.04932/"&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>
