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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hypermodels for Exploration · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2006.07464/</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/2006.07464/feed.xml" rel="self" type="application/rss+xml" /><item><title>Hypermodels for Exploration — mentioned by nmca</title><link>https://news.ycombinator.com/item?id=26027911</link><guid isPermaLink="false">urn:hackerpapers:mention:2006.07464:26027911</guid><pubDate>Thu, 04 Feb 2021 17:56:53 GMT</pubDate><description>&lt;p&gt;Hey Mike - I did some work on an industry active learning system a few years ago. The high level finding was that transfer &amp;amp; nonparametric methods were huge wins, but online uncertainty and &amp;quot;real active learning&amp;quot; hardly worked at all and were super complicated (at least in caffe1 anyway lol). Can you point to any big breakthroughs that have helped in recent years? Linear Hypermodels (https://arxiv.org/abs/2006.07464) seem promising, but that original experience has left me with some healthy skepticism.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2006.07464/"&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>
