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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Convolutional Kernel Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1406.3332/</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/1406.3332/feed.xml" rel="self" type="application/rss+xml" /><item><title>Convolutional Kernel Networks — mentioned by kastnerkyle</title><link>https://news.ycombinator.com/item?id=8152315</link><guid isPermaLink="false">urn:hackerpapers:mention:1406.3332:8152315</guid><pubDate>Fri, 08 Aug 2014 11:34:42 GMT</pubDate><description>&lt;p&gt;I find it interesting that no group (to my knowledge) has tried something similar to [Do Deep Networks Need to Be Deep?](http://arxiv.org/abs/1312.6184) for ImageNet scale networks. There have been several results which show that the knowledge learned in larger networks can be compressed and approximated using small or even single layer nets. Extreme learning machines (ELM) can be seen as another aspect of this. There have also been interesting results in the &amp;quot;kernelization&amp;quot; of convnets [from Julian Mairal and co.](http://arxiv.org/abs/1406.3332) that, accompanied by the stong crossover…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1406.3332/"&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>
