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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>REPAIR: REnormalizing Permuted Activations for Interpolation Repair · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2211.08403/</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/2211.08403/feed.xml" rel="self" type="application/rss+xml" /><item><title>REPAIR: REnormalizing Permuted Activations for Interpolation Repair — mentioned by dontwearitout</title><link>https://news.ycombinator.com/item?id=40569355</link><guid isPermaLink="false">urn:hackerpapers:mention:2211.08403:40569355</guid><pubDate>Tue, 04 Jun 2024 00:25:27 GMT</pubDate><description>&lt;p&gt;I haven&amp;#x27;t heard the term &amp;quot;shallow basin hypothesis&amp;quot; but I know what it refers to, these two papers spring to mind for me: 1) Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs https://arxiv.org/abs/1802.10026 2) Visualizing the Loss Landscape of Neural Nets https://arxiv.org/abs/1712.09913 There&amp;#x27;s also a very interesting body of work on merging trained models, such as by interpolating between points in weight space, which relates to the concept of &amp;quot;basins&amp;quot; of similar solutions. Skim the intro of this if you&amp;#x27;re interested in learning more: https://arxiv.org/abs/2211.08403&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2211.08403/"&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>
