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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian Deep Learning and a Probabilistic Perspective of Generalization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2002.08791/</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/2002.08791/feed.xml" rel="self" type="application/rss+xml" /><item><title>Bayesian Deep Learning and a Probabilistic Perspective of Generalization — mentioned by yldedly</title><link>https://news.ycombinator.com/item?id=28608298</link><guid isPermaLink="false">urn:hackerpapers:mention:2002.08791:28608298</guid><pubDate>Tue, 21 Sep 2021 18:43:43 GMT</pubDate><description>&lt;p&gt;DD disappears when doing Bayesian model averaging: https://arxiv.org/pdf/2002.08791.pdf It seems DD is a phenomenon specific to point estimates. &amp;gt;overfitting problem is really in terms of model flexibility versus data complexity, and not necessarily in terms of number of parameters per se versus sample size Yep, well put.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2002.08791/"&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>Bayesian Deep Learning and a Probabilistic Perspective of Generalization — HN submission</title><link>https://news.ycombinator.com/item?id=22386004</link><guid isPermaLink="false">urn:hackerpapers:submission:2002.08791:22386004</guid><pubDate>Fri, 21 Feb 2020 19:32:01 GMT</pubDate><description>&lt;p&gt;Bayesian Deep Learning and a Probabilistic Perspective of Generalization&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2002.08791/"&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>
