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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2301.12530/</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/2301.12530/feed.xml" rel="self" type="application/rss+xml" /><item><title>G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network — mentioned by synctext</title><link>https://news.ycombinator.com/item?id=49786171</link><guid isPermaLink="false">urn:hackerpapers:mention:2301.12530:49786171</guid><pubDate>Mon, 21 Sep 2026 12:02:03 GMT</pubDate><description>&lt;p&gt;Using the term AGI and not including any performance analysis. My AI calls it: &amp;quot;massive marketing overreach&amp;quot;. Somebody called this slop in the comments. As a professor who published on continual learning I&amp;#x27;m leaning towards agreement[1]. It lacks any substance. No relation to related work, no description of algorithm, no ablation study, just hand-waving that we&amp;#x27;re feeding some data and &amp;quot;Chess is not forgotten&amp;quot;. This &amp;quot;how-continual-learning-works&amp;quot; markdown text is not an algorithm [2]. [1] https://arxiv.org/abs/2301.12530 [2] https://github.com/volotat/mini-AGI/#how-continual-learning-...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2301.12530/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network — mentioned by synctext</title><link>https://news.ycombinator.com/item?id=34800727</link><guid isPermaLink="false">urn:hackerpapers:mention:2301.12530:34800727</guid><pubDate>Wed, 15 Feb 2023 06:27:56 GMT</pubDate><description>&lt;p&gt;Thnx for writing! In academia we&amp;#x27;re getting the next step operational: training on Android. Any advise for us what to watch out for? Obviously you need a bit of patience and lots of volunteer devices. With unsupervised continuous learning this is solved, in emulation. See &amp;quot;G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network&amp;quot; [1]. Optimal learning rate is left as an exercise for the developer. (disclaimer: our own work, I run a lab with &amp;quot;systems for peer-to-peer machine learning&amp;quot;) [1] https://arxiv.org/abs/2301.12530&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2301.12530/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network — mentioned by synctext</title><link>https://news.ycombinator.com/item?id=34593222</link><guid isPermaLink="false">urn:hackerpapers:mention:2301.12530:34593222</guid><pubDate>Tue, 31 Jan 2023 09:46:24 GMT</pubDate><description>&lt;p&gt;Fascinating material, significant progress in learn-to-rank and semantic clustering lately. For those who want to dive deeper, state-of-the-art decentralised learning from academia: https://arxiv.org/abs/2301.12530 &amp;quot;G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network&amp;quot;, published today. Disclaimer: I&amp;#x27;m the supervising professor.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2301.12530/"&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>
