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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Beyond Graph Neural Networks with Lifted Relational Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2007.06286/</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/2007.06286/feed.xml" rel="self" type="application/rss+xml" /><item><title>Beyond Graph Neural Networks with Lifted Relational Neural Networks — mentioned by joaquincabezas</title><link>https://news.ycombinator.com/item?id=31093749</link><guid isPermaLink="false">urn:hackerpapers:mention:2007.06286:31093749</guid><pubDate>Wed, 20 Apr 2022 07:15:17 GMT</pubDate><description>&lt;p&gt;The GCN implementation is based on the concept of templating (or lifting) which is not exploited in Scallop. In fact, this is the key idea of Neuralogic for joining deep learning and relational-logic representations. It is explained in the paper &amp;quot;Beyond Graph Neural Networks with Lifted Relational Neural Networks&amp;quot; (https://arxiv.org/abs/2007.06286) and you also have a series of blog posts at https://medium.com/@sir.gustav&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2007.06286/"&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>
