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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Variational Bayesian Last Layers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2404.11599/</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/2404.11599/feed.xml" rel="self" type="application/rss+xml" /><item><title>Variational Bayesian Last Layers — mentioned by zxexz</title><link>https://news.ycombinator.com/item?id=49809862</link><guid isPermaLink="false">urn:hackerpapers:mention:2404.11599:49809862</guid><pubDate>Tue, 22 Sep 2026 23:54:53 GMT</pubDate><description>&lt;p&gt;This whole thing reminds me of DeepMind’s Variational Bayesian Last Layers[0], which never gained much traction in the broader “AI” world, but is a remarkably useful tool. And a relatively obvious one that anyone with experience in SVI and with transformer pretraining, seems to independently rediscover (including me) before finding this paper. [0] https://arxiv.org/pdf/2404.11599&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2404.11599/"&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>
