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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1003.0358/</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/1003.0358/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition — mentioned by fchollet</title><link>https://news.ycombinator.com/item?id=8927090</link><guid isPermaLink="false">urn:hackerpapers:mention:1003.0358:8927090</guid><pubDate>Thu, 22 Jan 2015 02:01:16 GMT</pubDate><description>&lt;p&gt;In any case, I think that&amp;#x27;s a point very much relevant to the analogy: when you do more of the same, but at a scale a few orders of magnitude larger, you do get qualitatively different results. That&amp;#x27;s very much applicable to AI. As an illustration, when we started to use GPUs for training neural nets with the exact same algorithms as a decade earlier but with an order of magnitude more parameters, we got dramatically better results (see Ciresan et al. 2010 [1]). Building the same dumb skyscrapers but making them 100 times taller might in fact get them to fly. [1] http://arxiv.org/abs/1003.0358&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1003.0358/"&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>
