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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Relational recurrent neural networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1806.01822/</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/1806.01822/feed.xml" rel="self" type="application/rss+xml" /><item><title>Relational recurrent neural networks — mentioned by cute_boi</title><link>https://news.ycombinator.com/item?id=48822131</link><guid isPermaLink="false">urn:hackerpapers:mention:1806.01822:48822131</guid><pubDate>Tue, 07 Jul 2026 19:03:23 GMT</pubDate><description>&lt;p&gt;No need stupid moving texts. CS231n: Convolutional Neural Networks for Visual Recognition - https://cs231n.github.io/ The Unreasonable Effectiveness of Recurrent Neural Networks - https://karpathy.github.io/2015/05/21/rnn-effectiveness/ Understanding LSTM Networks - https://colah.github.io/posts/2015-08-Understanding-LSTMs/ ImageNet Classification with Deep Convolutional Neural Networks - https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436... Deep Residual Learning for Image Recognition - https://arxiv.org/abs/1512.03385 Multi-Scale Context Aggregation by Dilated Convolutions…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1806.01822/"&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>Relational recurrent neural networks — mentioned by gooob</title><link>https://news.ycombinator.com/item?id=48822006</link><guid isPermaLink="false">urn:hackerpapers:mention:1806.01822:48822006</guid><pubDate>Tue, 07 Jul 2026 18:54:32 GMT</pubDate><description>&lt;p&gt;https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436... https://papers.nips.cc/paper_files/paper/2012/file/c399862d3... https://arxiv.org/pdf/1512.03385 https://arxiv.org/pdf/1511.07122 https://arxiv.org/pdf/1603.05027 https://arxiv.org/pdf/1409.2329 https://arxiv.org/pdf/1512.02595 https://arxiv.org/pdf/1409.0473 https://arxiv.org/pdf/1506.03134 https://arxiv.org/pdf/1706.03762 https://nlp.seas.harvard.edu/annotated-transformer/ https://arxiv.org/pdf/1410.5401 https://arxiv.org/pdf/1706.01427 https://arxiv.org/pdf/1806.01822…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1806.01822/"&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>Relational recurrent neural networks — mentioned by is_true</title><link>https://news.ycombinator.com/item?id=40398434</link><guid isPermaLink="false">urn:hackerpapers:mention:1806.01822:40398434</guid><pubDate>Sat, 18 May 2024 12:26:29 GMT</pubDate><description>&lt;p&gt;a backup just in case https://nlp.seas.harvard.edu/annotated-transformer/ https://scottaaronson.blog/?p=762 https://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://arxiv.org/pdf/1409.2329.pdf https://www.cs.toronto.edu/~hinton/absps/colt93.pdf https://arxiv.org/pdf/1506.03134.pdf https://proceedings.neurips.cc/paper_files/paper/2012/file/c... https://arxiv.org/pdf/1511.06391.pdf https://arxiv.org/pdf/1811.06965.pdf https://arxiv.org/pdf/1512.03385.pdf https://arxiv.org/pdf/1511.07122.pdf https://arxiv.org/pdf/1704.01212.pdf…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1806.01822/"&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>Relational recurrent neural networks — mentioned by yonkshi</title><link>https://news.ycombinator.com/item?id=18749943</link><guid isPermaLink="false">urn:hackerpapers:mention:1806.01822:18749943</guid><pubDate>Mon, 24 Dec 2018 02:50:23 GMT</pubDate><description>&lt;p&gt;Sure. I don&amp;#x27;t have any holistic survey to prove my point, but an example of recent progress in terms of sample efficiency is this paper[0]. Derivatives of this paper have been used to solve Sudoku[1], Starcraft II[2] and more [3]. This paper enabled more efficient use of data by creating a probabilistic graphical model between logical sets. [0]https://arxiv.org/abs/1706.01427 [1]https://arxiv.org/abs/1711.08028 [2]https://arxiv.org/abs/1806.01830 [3]https://arxiv.org/abs/1806.01822&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1806.01822/"&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>
