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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Group Equivariant Convolutional Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1602.07576/</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/1602.07576/feed.xml" rel="self" type="application/rss+xml" /><item><title>Group Equivariant Convolutional Networks — mentioned by niszoigg</title><link>https://news.ycombinator.com/item?id=37926854</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.07576:37926854</guid><pubDate>Wed, 18 Oct 2023 10:28:29 GMT</pubDate><description>&lt;p&gt;This video is meant to be a supplementary resource to help understanding the below paper by Taco S. Cohen and Max Welling [Group Equivariant Convolutional Networks](https://arxiv.org/abs/1602.07576) For me, this video is also a save-game-fountain en route my journey into the broader topic of Geometric Deep Learning.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.07576/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Group Equivariant Convolutional Networks — mentioned by niszoig2</title><link>https://news.ycombinator.com/item?id=37749969</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.07576:37749969</guid><pubDate>Tue, 03 Oct 2023 10:23:22 GMT</pubDate><description>&lt;p&gt;The video is meant to be a supplementary aid to help understanding [this](https://arxiv.org/abs/1602.07576) paper by Taco S. Cohen and Max Welling. For me, the video is a save-game-fountain en route my journey into the broader topic of Geometric Deep Learning&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.07576/"&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>
