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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Information-theoretical label embeddings for large-scale image classification · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1607.05691/</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/1607.05691/feed.xml" rel="self" type="application/rss+xml" /><item><title>Information-theoretical label embeddings for large-scale image classification — mentioned by Smerity</title><link>https://news.ycombinator.com/item?id=12138633</link><guid isPermaLink="false">urn:hackerpapers:mention:1607.05691:12138633</guid><pubDate>Thu, 21 Jul 2016 17:48:44 GMT</pubDate><description>&lt;p&gt;Some people are noting that TensorFlow evens the playing field, allowing other companies to poach Google&amp;#x27;s employees. Whilst that is true to some light degree, there are amazingly large caveats - Google still have the major advantage when it comes to poaching. The other important note is that open sourcing TensorFlow is likely more important for attracting recent graduates in machine learning than it is for attracting employees of other companies. Most machine learning engineers and researchers get excited by two things: underlying hardware and underlying tooling. Google have enough GPUs to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1607.05691/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
