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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Not all layers are equally as important: Every Layer Counts BERT · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2311.02265/</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/2311.02265/feed.xml" rel="self" type="application/rss+xml" /><item><title>Not all layers are equally as important: Every Layer Counts BERT — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=39807426</link><guid isPermaLink="false">urn:hackerpapers:mention:2311.02265:39807426</guid><pubDate>Sun, 24 Mar 2024 14:25:31 GMT</pubDate><description>&lt;p&gt;Yeah, you can&amp;#x27;t change the model much with low LRs. That&amp;#x27;s the point! Same reason you don&amp;#x27;t get continual-learning if you just keep using low LRs: https://arxiv.org/abs/2403.08763 You need to really shake up the model if you want to learn some genuinely better (ie. different) internal representations that exploits the DenseNet (https://arxiv.org/abs/1608.06993)/LTG-BERT (https://arxiv.org/abs/2311.02265) arch you&amp;#x27;re using here.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2311.02265/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
