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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Brief Review of Hypernetworks in Deep Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2306.06955/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Wed, 04 Nov 2026 05:35:47 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2306.06955/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Brief Review of Hypernetworks in Deep Learning — mentioned by yurimo</title><link>https://news.ycombinator.com/item?id=50348088</link><guid isPermaLink="false">urn:hackerpapers:mention:2306.06955:50348088</guid><pubDate>Tue, 03 Nov 2026 04:30:24 GMT</pubDate><description>&lt;p&gt;It does, one of my ongoing research papers is about it. But I would ask people to take a look at the established idea of Hypernetworks: https://arxiv.org/pdf/2306.06955v3 Modern version of the field comes from Google Brain around 2016 https://arxiv.org/pdf/1609.09106. In general my personal research thesis is that current deep learning is much less malleable and neuroplastic as it should be, I got into interpretability because of this. Many focus on safety, but I prioritize it as a toolbox that allows for greater shaping of the underlying intelligence system. If we can fully understand how…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2306.06955/"&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>
