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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Training highly effective connectivities within neural networks with randomly initialized, fixed weights · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2006.16627/</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/2006.16627/feed.xml" rel="self" type="application/rss+xml" /><item><title>Training highly effective connectivities within neural networks with randomly initialized, fixed weights — mentioned by kandu</title><link>https://news.ycombinator.com/item?id=39543596</link><guid isPermaLink="false">urn:hackerpapers:mention:2006.16627:39543596</guid><pubDate>Wed, 28 Feb 2024 21:22:18 GMT</pubDate><description>&lt;p&gt;Also: training neural networks by turning connections on and off, or by just flipping the sign of the weights: https://arxiv.org/abs/2006.16627&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2006.16627/"&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>
