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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1606.09375/</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/1606.09375/feed.xml" rel="self" type="application/rss+xml" /><item><title>Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering — mentioned by gspr</title><link>https://news.ycombinator.com/item?id=28245252</link><guid isPermaLink="false">urn:hackerpapers:mention:1606.09375:28245252</guid><pubDate>Fri, 20 Aug 2021 12:28:35 GMT</pubDate><description>&lt;p&gt;&amp;gt; any recommendations on papers / tutorials There&amp;#x27;s essentially two sides to the story: the convolutional deep network side and the topological side. The former is is excellently covered by my coauthor in another paper of his (https://arxiv.org/abs/1606.09375), and the latter is covered by many a textbook or intro text (like http://graphics.stanford.edu/courses/cs164-09-spring/Handout...). &amp;gt; + any libs, esp. scalable? https://paperswithcode.com/paper/simplicial-neural-networks The code isn&amp;#x27;t very performant, but there&amp;#x27;s lots of room for improvement. It&amp;#x27;s more a proof of concept, with lots of…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1606.09375/"&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>
