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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Optimization Methods for Large-Scale Machine Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1606.04838/</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/1606.04838/feed.xml" rel="self" type="application/rss+xml" /><item><title>Optimization Methods for Large-Scale Machine Learning — mentioned by smallcharleston</title><link>https://news.ycombinator.com/item?id=21954538</link><guid isPermaLink="false">urn:hackerpapers:mention:1606.04838:21954538</guid><pubDate>Sat, 04 Jan 2020 12:25:33 GMT</pubDate><description>&lt;p&gt;I’m not that other guy and I also haven’t read this paper but it seems quite thorough https://arxiv.org/abs/1606.04838&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1606.04838/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>Optimization Methods for Large-Scale Machine Learning — HN submission</title><link>https://news.ycombinator.com/item?id=11945860</link><guid isPermaLink="false">urn:hackerpapers:submission:1606.04838:11945860</guid><pubDate>Tue, 21 Jun 2016 14:31:56 GMT</pubDate><description>&lt;p&gt;Optimization Methods for Large-Scale Machine Learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;38 points; 7 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1606.04838/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>Optimization Methods for Large-Scale Machine Learning — mentioned by imurray</title><link>https://news.ycombinator.com/item?id=11945549</link><guid isPermaLink="false">urn:hackerpapers:mention:1606.04838:11945549</guid><pubDate>Tue, 21 Jun 2016 13:50:07 GMT</pubDate><description>&lt;p&gt;For a mathy academic survey of gradient descent methods (from this month, June 2016), I recommend: https://arxiv.org/abs/1606.04838 (It was submitted by someone else a few days ago, but saw no traction.)&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1606.04838/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>Optimization Methods for Large-Scale Machine Learning — HN submission</title><link>https://news.ycombinator.com/item?id=11917330</link><guid isPermaLink="false">urn:hackerpapers:submission:1606.04838:11917330</guid><pubDate>Thu, 16 Jun 2016 16:57:03 GMT</pubDate><description>&lt;p&gt;Optimization Methods for Large-Scale Machine Learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1606.04838/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
