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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generalized Denoising Auto-Encoders as Generative Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1305.6663/</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/1305.6663/feed.xml" rel="self" type="application/rss+xml" /><item><title>Generalized Denoising Auto-Encoders as Generative Models — mentioned by mbeissinger</title><link>https://news.ycombinator.com/item?id=6730158</link><guid isPermaLink="false">urn:hackerpapers:mention:1305.6663:6730158</guid><pubDate>Thu, 14 Nov 2013 02:30:22 GMT</pubDate><description>&lt;p&gt;Yes this is really interesting. I haven&amp;#x27;t read those other papers yet (definitely plan on it now thanks for the links), but Bengio&amp;#x27;s latest paper on denoising autoencoders from earlier this year (http://arxiv.org/abs/1305.6663) still used the unsupervised pretraining. Also the Theano implementation that I run experiments with uses it as well (but that code could be a year or two old). Definitely going to be researching this more throughout the year.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1305.6663/"&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>
