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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Designing Neural Network Architectures using Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.02167/</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/1611.02167/feed.xml" rel="self" type="application/rss+xml" /><item><title>Designing Neural Network Architectures using Reinforcement Learning — mentioned by chriskanan</title><link>https://news.ycombinator.com/item?id=14772160</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.02167:14772160</guid><pubDate>Fri, 14 Jul 2017 18:35:50 GMT</pubDate><description>&lt;p&gt;There has been some work related to what you are describing: Learning to learn by gradient descent by gradient descent https://arxiv.org/abs/1606.04474 Related (but less so), there are also some papers about learning neural network architectures: Designing Neural Network Architectures using Reinforcement Learning https://arxiv.org/abs/1611.02167 Neural Architecture Search with Reinforcement Learning https://arxiv.org/abs/1611.01578&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.02167/"&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>Designing Neural Network Architectures using Reinforcement Learning — HN submission</title><link>https://news.ycombinator.com/item?id=13454793</link><guid isPermaLink="false">urn:hackerpapers:submission:1611.02167:13454793</guid><pubDate>Sun, 22 Jan 2017 11:45:35 GMT</pubDate><description>&lt;p&gt;Designing Neural Network Architectures Using Reinforcement Learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;5 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1611.02167/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Designing Neural Network Architectures using Reinforcement Learning — mentioned by lucidrains</title><link>https://news.ycombinator.com/item?id=13305788</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.02167:13305788</guid><pubDate>Tue, 03 Jan 2017 00:55:52 GMT</pubDate><description>&lt;p&gt;Other equally exciting papers that relates to learning to learn in DL. &amp;quot;Neural Architecture Search with Reinforcement Learning&amp;quot; https://arxiv.org/abs/1611.01578 &amp;quot;RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning&amp;quot; https://arxiv.org/abs/1611.02779 &amp;quot;Designing Neural Network Architectures using Reinforcement Learning&amp;quot; https://arxiv.org/abs/1611.02167&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.02167/"&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>Designing Neural Network Architectures using Reinforcement Learning — HN submission</title><link>https://news.ycombinator.com/item?id=13104305</link><guid isPermaLink="false">urn:hackerpapers:submission:1611.02167:13104305</guid><pubDate>Mon, 05 Dec 2016 09:25:25 GMT</pubDate><description>&lt;p&gt;Neural Networks Designed Using Reinforcement Learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1611.02167/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Designing Neural Network Architectures using Reinforcement Learning — mentioned by lucidrains</title><link>https://news.ycombinator.com/item?id=13097605</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.02167:13097605</guid><pubDate>Sat, 03 Dec 2016 21:33:54 GMT</pubDate><description>&lt;p&gt;absolutely dude.. for example, this paper just popped up in the last week https://arxiv.org/abs/1611.02167&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.02167/"&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>
