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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2402.01484/</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/2402.01484/feed.xml" rel="self" type="application/rss+xml" /><item><title>Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? — mentioned by nextfx</title><link>https://news.ycombinator.com/item?id=42167535</link><guid isPermaLink="false">urn:hackerpapers:mention:2402.01484:42167535</guid><pubDate>Sun, 17 Nov 2024 21:36:59 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;m not an expert on MCMC for BNNs by any means, but even with small networks I think it&amp;#x27;s a little tricky to get right. If my memory serves me right, this paper[1] focuses on small networks and goes over the issues and how to get around them. [1]: https://arxiv.org/abs/2402.01484&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2402.01484/"&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>
