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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1506.02158/</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/1506.02158/feed.xml" rel="self" type="application/rss+xml" /><item><title>Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference — mentioned by epmaybe</title><link>https://news.ycombinator.com/item?id=15663995</link><guid isPermaLink="false">urn:hackerpapers:mention:1506.02158:15663995</guid><pubDate>Thu, 09 Nov 2017 17:05:17 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;ve read the article :), the author is the same physician who wrote &amp;quot;emperor of all maladies&amp;quot;, a book about cancer. Basically what it comes down to is that doctors get really good at bayesian inference at some point in their career, definitely not while we are students. I am however confident that we can build models that learn in a similar manner. I&amp;#x27;m actually trying to build a model for a class project that uses bayesian inference in a CNN as per Gal et al (https://arxiv.org/abs/1506.02158) to try and detect diabetic retinopathy. Their paper actually tries to detect different skin cancers.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1506.02158/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
