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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>RePAIR: Predictive Self-Supervised Representation Learning in Chess · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2606.11860/</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/2606.11860/feed.xml" rel="self" type="application/rss+xml" /><item><title>RePAIR: Predictive Self-Supervised Representation Learning in Chess — mentioned by vatsachak</title><link>https://news.ycombinator.com/item?id=48693103</link><guid isPermaLink="false">urn:hackerpapers:mention:2606.11860:48693103</guid><pubDate>Fri, 26 Jun 2026 23:00:29 GMT</pubDate><description>&lt;p&gt;JEPA in chess leads to interpretable chess boards: https://arxiv.org/abs/2606.11860 JEPA in image classification leads to interpretable image latents https://arxiv.org/abs/2508.10104 Easy intro to JEPA, demonstrating that interpretability is as easy as running a PCA on latents https://youtu.be/kYkIdXwW2AE?is=CfCBcy1jLt-FfI2E&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2606.11860/"&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>
