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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2301.13703/</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/2301.13703/feed.xml" rel="self" type="application/rss+xml" /><item><title>Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning — mentioned by esafak</title><link>https://news.ycombinator.com/item?id=43928045</link><guid isPermaLink="false">urn:hackerpapers:mention:2301.13703:43928045</guid><pubDate>Thu, 08 May 2025 16:43:29 GMT</pubDate><description>&lt;p&gt;In general you do, because the unbiased estimates have higher generalization error. You are already dealing with sampling noise. I am not an expert in optimization, and what &amp;quot;poorly understood&amp;quot; means to you, but I know there is quite some research on the properties of SGD noise; e.g., https://francisbach.com/rethinking-sgd-noise/ Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning https://arxiv.org/abs/2301.13703&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2301.13703/"&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>
