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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Differentiable plasticity: training plastic neural networks with backpropagation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1804.02464/</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/1804.02464/feed.xml" rel="self" type="application/rss+xml" /><item><title>Differentiable plasticity: training plastic neural networks with backpropagation — mentioned by dpflan</title><link>https://news.ycombinator.com/item?id=16803502</link><guid isPermaLink="false">urn:hackerpapers:mention:1804.02464:16803502</guid><pubDate>Tue, 10 Apr 2018 17:58:41 GMT</pubDate><description>&lt;p&gt;Interesting. Some highlighted links from the writeup: 1. Differentiable plasticity: training plastic neural networks with backpropagation (https://arxiv.org/abs/1804.02464) 2. Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (https://arxiv.org/abs/1703.10371) 3. Github for the project: https://github.com/uber-common/differentiable-plasticity 4. Learning to Learn (http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/) 5. Meta-Learning: http://metalearning.ml/&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1804.02464/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.NE</category></item></channel></rss>
