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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Learned Index for Exact Similarity Search in Metric Spaces · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2204.10028/</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/2204.10028/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Learned Index for Exact Similarity Search in Metric Spaces — mentioned by Lichtso</title><link>https://news.ycombinator.com/item?id=39795084</link><guid isPermaLink="false">urn:hackerpapers:mention:2204.10028:39795084</guid><pubDate>Fri, 22 Mar 2024 21:32:51 GMT</pubDate><description>&lt;p&gt;Here is one of the recent advancements in performing similarity (including nearest neighbor) search on sparse high dimensional data using learned indices: https://arxiv.org/abs/2204.10028 The paper basically deals with the most adverse case but the approach should work in lower dimensions and in data with a non-uniform density distribution as well.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2204.10028/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DB</category></item></channel></rss>
