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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Pruning Convolutional Neural Networks for Resource Efficient Inference · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.06440/</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/1611.06440/feed.xml" rel="self" type="application/rss+xml" /><item><title>Pruning Convolutional Neural Networks for Resource Efficient Inference — mentioned by mkagenius</title><link>https://news.ycombinator.com/item?id=20052953</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.06440:20052953</guid><pubDate>Thu, 30 May 2019 16:33:53 GMT</pubDate><description>&lt;p&gt;Can this now be combined with pruning the trained network to even further the size reduction by 5x? [1] 1. https://arxiv.org/abs/1611.06440 - Pruning Convolutional Neural Networks for Resource Efficient Inference&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.06440/"&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>
