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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Efficient Neural Architecture Search via Proximal Iterations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1905.13577/</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/1905.13577/feed.xml" rel="self" type="application/rss+xml" /><item><title>Efficient Neural Architecture Search via Proximal Iterations — mentioned by AbrahamParangi</title><link>https://news.ycombinator.com/item?id=21029978</link><guid isPermaLink="false">urn:hackerpapers:mention:1905.13577:21029978</guid><pubDate>Fri, 20 Sep 2019 18:49:51 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;ll go further: For each problem there are a few sub-problems and a &amp;quot;right way&amp;quot; to do each of them where a &amp;quot;right way&amp;quot; corresponds to an algorithm which efficiently tackles some element of the implicit structure of the problem. I think within 5 years we will have made great progress on algorithmically discovering these &amp;quot;right ways&amp;quot; across many problem domains. I think both differentiable neural architecture search[0] and the MAML with implicit gradients[1] stuff is very interesting from this perspective. [0]https://arxiv.org/abs/1905.13577 [1]https://arxiv.org/abs/1909.04630&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1905.13577/"&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>
