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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.00712/</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/1611.00712/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables — mentioned by eli_gottlieb</title><link>https://news.ycombinator.com/item?id=13123866</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.00712:13123866</guid><pubDate>Wed, 07 Dec 2016 16:44:16 GMT</pubDate><description>&lt;p&gt;Yeah, not to be too dismissive or cliched, but these slides come off as if LeCun just now got around to reading some Andy Clark or reading neurosci/cogsci papers by Tenenbaum or Friston. The idea that the human brain has to work via active prediction rather than passive signal processing has been well-established in cogsci and neurosci for a while now. The interesting question, then, is how we make machine-learning systems do prediction well. Probabilistic/generative models have been &amp;quot;wandering in the desert&amp;quot; for a while now because Monte Carlo methods are just so slow, especially for…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.00712/"&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>
