<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Position: Topological Deep Learning is the New Frontier for Relational Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2402.08871/</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/2402.08871/feed.xml" rel="self" type="application/rss+xml" /><item><title>Position: Topological Deep Learning is the New Frontier for Relational Learning — mentioned by trashtester</title><link>https://news.ycombinator.com/item?id=41454946</link><guid isPermaLink="false">urn:hackerpapers:mention:2402.08871:41454946</guid><pubDate>Thu, 05 Sep 2024 09:19:32 GMT</pubDate><description>&lt;p&gt;&amp;gt; Who would have thought that vectorized linear algebra will be at the center of so much financial speculation? &amp;quot;vectorized linear algebra&amp;quot; is at the root of most of modern Physics. Specifically, the laws of Physics are represented by the Lie groups U(1), SU(2), SU(3) and SO(3,1). While the manifolds that Physics act on are curved, they&amp;#x27;re &amp;quot;locally flat&amp;quot;. That is why local operations are tensor operations. Or linear algebra, if you prefer. It&amp;#x27;s not all that surprising to me that &amp;quot;intelligence&amp;quot; is represented by similar math. In fact, there is active work being done on making sense of deep…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2402.08871/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
