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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Activation Functions: A new paradigm for understanding Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1906.09529/</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/1906.09529/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning Activation Functions: A new paradigm for understanding Neural Networks — mentioned by porridgeraisin</title><link>https://news.ycombinator.com/item?id=43195700</link><guid isPermaLink="false">urn:hackerpapers:mention:1906.09529:43195700</guid><pubDate>Thu, 27 Feb 2025 16:12:21 GMT</pubDate><description>&lt;p&gt;This idea exists (the broad field is called neural architecture search), although you have to parameterize it somehow to allow gradient descent to happen. Here are examples: https://arxiv.org/abs/2009.04759 https://arxiv.org/abs/1906.09529&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1906.09529/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Learning Activation Functions: A new paradigm for understanding Neural Networks — mentioned by adamzen</title><link>https://news.ycombinator.com/item?id=34713299</link><guid isPermaLink="false">urn:hackerpapers:mention:1906.09529:34713299</guid><pubDate>Wed, 08 Feb 2023 19:17:39 GMT</pubDate><description>&lt;p&gt;Learned activation functions do seem to be a thing(https://arxiv.org/abs/1906.09529)&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1906.09529/"&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>
