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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2605.08633/</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/2605.08633/feed.xml" rel="self" type="application/rss+xml" /><item><title>Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction — mentioned by adrian_b</title><link>https://news.ycombinator.com/item?id=48714796</link><guid isPermaLink="false">urn:hackerpapers:mention:2605.08633:48714796</guid><pubDate>Mon, 29 Jun 2026 04:27:05 GMT</pubDate><description>&lt;p&gt;Additional details in: https://arxiv.org/abs/2605.08633v1 https://www.servethehome.com/arm-cpus-take-number-1-in-lates... https://www.top500.org/news/lineshine-debuts-no-1-top500-ent...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2605.08633/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item><item><title>Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction — mentioned by adrian_b</title><link>https://news.ycombinator.com/item?id=48689881</link><guid isPermaLink="false">urn:hackerpapers:mention:2605.08633:48689881</guid><pubDate>Fri, 26 Jun 2026 18:05:24 GMT</pubDate><description>&lt;p&gt;Additional details at: https://www.servethehome.com/arm-cpus-take-number-1-in-lates... Also at: https://arxiv.org/abs/2605.08633v1 This uses a custom Armv9-A CPU with SME (scalable matrix extension), which needs no GPU to reach that performance, unlike the AMD systems that follow beneath it.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2605.08633/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item><item><title>Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction — HN submission</title><link>https://news.ycombinator.com/item?id=48124774</link><guid isPermaLink="false">urn:hackerpapers:submission:2605.08633:48124774</guid><pubDate>Wed, 13 May 2026 17:20:28 GMT</pubDate><description>&lt;p&gt;Exascale Training of Generative Model with Historical Priors for Data Reduction&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2605.08633/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item></channel></rss>
