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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Large Batch Training of Convolutional Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1708.03888/</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/1708.03888/feed.xml" rel="self" type="application/rss+xml" /><item><title>Large Batch Training of Convolutional Networks — mentioned by samsamoa</title><link>https://news.ycombinator.com/item?id=18684358</link><guid isPermaLink="false">urn:hackerpapers:mention:1708.03888:18684358</guid><pubDate>Fri, 14 Dec 2018 20:25:50 GMT</pubDate><description>&lt;p&gt;Author here— Yes, this is a great question! I think there&amp;#x27;s a lot more interesting work to be done here. It would be great if we could understand e.g. why layer-wise learning rates help with large-batch training of ImageNet (https://arxiv.org/abs/1708.03888), and maybe the per-component noise scale has something to do with it.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1708.03888/"&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>
