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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Evidential Deep Learning to Quantify Classification Uncertainty · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1806.01768/</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/1806.01768/feed.xml" rel="self" type="application/rss+xml" /><item><title>Evidential Deep Learning to Quantify Classification Uncertainty — mentioned by tdgunes</title><link>https://news.ycombinator.com/item?id=18554172</link><guid isPermaLink="false">urn:hackerpapers:mention:1806.01768:18554172</guid><pubDate>Wed, 28 Nov 2018 18:28:18 GMT</pubDate><description>&lt;p&gt;For people who are interested, there is an upcoming NIPS paper that might be relevant for this problem: Paper: http://arxiv.org/abs/1806.01768 Demonstration: https://muratsensoy.github.io/uncertainty.html&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1806.01768/"&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>
