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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>CRISP: Clustering Multi-Vector Representations for Denoising and Pruning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2505.11471/</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/2505.11471/feed.xml" rel="self" type="application/rss+xml" /><item><title>CRISP: Clustering Multi-Vector Representations for Denoising and Pruning — mentioned by adsharma</title><link>https://news.ycombinator.com/item?id=44043336</link><guid isPermaLink="false">urn:hackerpapers:mention:2505.11471:44043336</guid><pubDate>Tue, 20 May 2025 16:22:30 GMT</pubDate><description>&lt;p&gt;Isn&amp;#x27;t Deep Learning more like Graph Theory? I shared yesterday that Google published a paper called CRISP (https://arxiv.org/pdf/2505.11471) that carefully avoids any reference to the word &amp;quot;Graph&amp;quot;. So then the question becomes what&amp;#x27;s the difference between Graph Theory and Applied Topology? Graphs operate on discrete structures and topology is about a continuous space. Otherwise they&amp;#x27;re very closely related. But the higher order bit is that AI/ML and Deep Learning in particular could do a better job of learning from and acknowledging prior art from related fields. Reusing older terminology…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2505.11471/"&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>
