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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Prototypical Networks for Few-shot Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1703.05175/</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/1703.05175/feed.xml" rel="self" type="application/rss+xml" /><item><title>Prototypical Networks for Few-shot Learning — mentioned by Eridrus</title><link>https://news.ycombinator.com/item?id=16569438</link><guid isPermaLink="false">urn:hackerpapers:mention:1703.05175:16569438</guid><pubDate>Mon, 12 Mar 2018 16:48:23 GMT</pubDate><description>&lt;p&gt;Ok, I admit I hadn&amp;#x27;t looked up the actual numbers before I posted that comment, but the Snell paper wasn&amp;#x27;t the last word on that line of work, it was followed by https://arxiv.org/abs/1703.05175 (which doesn&amp;#x27;t have miniImagenet results). And there may have been further work in that direction that I&amp;#x27;m not aware of. You&amp;#x27;re right that Reptile is the simplest recent algorithm in the meta-learning literature, but I would argue that&amp;#x27;s basically my point, they started from somewhere pretty ambitious (lets learn a learner, or at least an SGD update rule), and ended up with learning an initialization…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1703.05175/"&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>
