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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Graph Capsule Convolutional Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1805.08090/</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/1805.08090/feed.xml" rel="self" type="application/rss+xml" /><item><title>Graph Capsule Convolutional Neural Networks — mentioned by stochastic_monk</title><link>https://news.ycombinator.com/item?id=18959682</link><guid isPermaLink="false">urn:hackerpapers:mention:1805.08090:18959682</guid><pubDate>Mon, 21 Jan 2019 11:46:51 GMT</pubDate><description>&lt;p&gt;Graph Capsule Convolutional Networks [0] claim to have beaten the state of the art in graph neural networks. However, I can’t find information on training time, which I think is significantly more expensive for capsule networks. [0] https://arxiv.org/abs/1805.08090&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1805.08090/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
