<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DeDrift: Robust Similarity Search under Content Drift · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2308.02752/</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/2308.02752/feed.xml" rel="self" type="application/rss+xml" /><item><title>DeDrift: Robust Similarity Search under Content Drift — mentioned by VoVAllen</title><link>https://news.ycombinator.com/item?id=42499546</link><guid isPermaLink="false">urn:hackerpapers:mention:2308.02752:42499546</guid><pubDate>Tue, 24 Dec 2024 03:35:51 GMT</pubDate><description>&lt;p&gt;Hi, I&amp;#x27;m the author of the article. Meta have conducted some experiments on dynamic IVF with datasets of several hundred million records. The conclusion was that recall can be maintained through simple repartitioning and rebalancing strategies. You can find more details here: DEDRIFT: Robust Similarity Search under Content Drift https://arxiv.org/pdf/2308.02752. Additionally, with the help of GPUs, KMeans can be computed quickly, making the cost of rebuilding the entire index acceptable in many cases.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2308.02752/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
