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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Unsupervised Dense Information Retrieval with Contrastive Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2112.09118/</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/2112.09118/feed.xml" rel="self" type="application/rss+xml" /><item><title>Unsupervised Dense Information Retrieval with Contrastive Learning — mentioned by svcrunch</title><link>https://news.ycombinator.com/item?id=33537561</link><guid isPermaLink="false">urn:hackerpapers:mention:2112.09118:33537561</guid><pubDate>Wed, 09 Nov 2022 20:43:57 GMT</pubDate><description>&lt;p&gt;This is an area that&amp;#x27;s dear to me, I&amp;#x27;m the cofounder of Vectara and have been working with embedding-based semantic search, aka neural search or neural IR, since 2017. To whether Google uses semantic search, the answer is yes, very heavily [1][2]. Not only that, but they have led, and continue to lead, much of the pioneering research in NLP and neural IR for the past decade [3][4][5]. Technical challenges lie along a few primary dimensions. The first has been search quality, because, while early neural systems like Google Talk to Books [5][6] demonstrated the potential of these techniques…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2112.09118/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.IR</category></item></channel></rss>
