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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cost-Effective Active Learning for Deep Image Classification · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1701.03551/</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/1701.03551/feed.xml" rel="self" type="application/rss+xml" /><item><title>Cost-Effective Active Learning for Deep Image Classification — mentioned by thiago_lira</title><link>https://news.ycombinator.com/item?id=17587816</link><guid isPermaLink="false">urn:hackerpapers:mention:1701.03551:17587816</guid><pubDate>Sun, 22 Jul 2018 17:31:10 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;m thinking about studying these for my MSc. A colleague has given me the idea of applying the Monte Carlo Dropout technique [1] (A interpretation of Dropout as a GP) to the CEAL framework [0]. On the other hand my mentor wanted me to work on a regression problem with a dataset from a cement factory. Have people been using GPs for problems like that? [0] https://arxiv.org/abs/1701.03551 (CEAL) [1] https://arxiv.org/pdf/1506.02142 (MC dropout)&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1701.03551/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
