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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Sparse Neural Networks through $L_0$ Regularization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1712.01312/</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/1712.01312/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning Sparse Neural Networks through $L_0$ Regularization — mentioned by dpkingma</title><link>https://news.ycombinator.com/item?id=41161404</link><guid isPermaLink="false">urn:hackerpapers:mention:1712.01312:41161404</guid><pubDate>Mon, 05 Aug 2024 13:54:23 GMT</pubDate><description>&lt;p&gt;There seems to be some relevant prior work that is not referenced by this paper, such as our work on training sparse neural networks: https://arxiv.org/abs/1712.01312 Abstract: &amp;quot;We propose a practical method for L0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. [...]&amp;quot;&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1712.01312/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
