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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Computational Optimal Transport · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1803.00567/</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/1803.00567/feed.xml" rel="self" type="application/rss+xml" /><item><title>Computational Optimal Transport — mentioned by skinner_</title><link>https://news.ycombinator.com/item?id=25555185</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.00567:25555185</guid><pubDate>Sun, 27 Dec 2020 23:38:05 GMT</pubDate><description>&lt;p&gt;&amp;gt; How cool! I was on the fence about whether or not to put up the source code— I&amp;#x27;m _glad_ I did! I&amp;#x27;m glad you did, thank you for that! Disclaimer: I&amp;#x27;m not claiming that any of my modifications actually help, there&amp;#x27;s too much randomness introduced by local minima, and I only did a few training runs. Unbiased Sinkhorn is fancier than Chamfer, but who knows if it&amp;#x27;s better or not for this use case. Starting from a much higher learning rate did speed up convergence, though. Re point cloud distance, there&amp;#x27;s lots of good stuff referenced in the GeomLoss documentation…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.00567/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>Computational Optimal Transport — HN submission</title><link>https://news.ycombinator.com/item?id=19975377</link><guid isPermaLink="false">urn:hackerpapers:submission:1803.00567:19975377</guid><pubDate>Tue, 21 May 2019 21:42:34 GMT</pubDate><description>&lt;p&gt;Computational Optimal Transport&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1803.00567/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
