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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2102.06356/</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/2102.06356/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes — mentioned by Mr_P</title><link>https://news.ycombinator.com/item?id=31579891</link><guid isPermaLink="false">urn:hackerpapers:mention:2102.06356:31579891</guid><pubDate>Wed, 01 Jun 2022 10:07:11 GMT</pubDate><description>&lt;p&gt;I think they should have let him publish the paper. We&amp;#x27;ve seen time and again that various trends in ML turn out to have actually been dead-ends. A few examples: https://arxiv.org/abs/2102.06356 https://arxiv.org/abs/1905.03678 That said, I&amp;#x27;ve also seen plenty of competitive drama in FAANG research labs, so this story is not hard to believe. More senior engineers often will use their seniority to power-grab control of projects. It sounds like Google execs did the right thing in the end.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2102.06356/"&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>
