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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Message Passing for Quantum Chemistry · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1704.01212/</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/1704.01212/feed.xml" rel="self" type="application/rss+xml" /><item><title>Neural Message Passing for Quantum Chemistry — mentioned by cute_boi</title><link>https://news.ycombinator.com/item?id=48822131</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.01212:48822131</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/1704.01212/"&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>Neural Message Passing for Quantum Chemistry — mentioned by gooob</title><link>https://news.ycombinator.com/item?id=48822006</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.01212:48822006</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/1704.01212/"&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>Neural Message Passing for Quantum Chemistry — mentioned by is_true</title><link>https://news.ycombinator.com/item?id=40398434</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.01212:40398434</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/1704.01212/"&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>Neural Message Passing for Quantum Chemistry — mentioned by eachro</title><link>https://news.ycombinator.com/item?id=25641516</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.01212:25641516</guid><pubDate>Tue, 05 Jan 2021 02:58:01 GMT</pubDate><description>&lt;p&gt;Has the author looked at how GNNs are used in say predicting molecular properties[1] or molecule generation[2]? GNNs are the state of the art on those tasks and those problems are certainly much harder than the Cora/Citeseer/etc semisupervised node label prediction tasks. [1] https://arxiv.org/abs/1704.01212 [2] http://proceedings.mlr.press/v80/jin18a/jin18a.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.01212/"&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>Neural Message Passing for Quantum Chemistry — mentioned by amitoz_azad</title><link>https://news.ycombinator.com/item?id=24234681</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.01212:24234681</guid><pubDate>Fri, 21 Aug 2020 13:26:13 GMT</pubDate><description>&lt;p&gt;My PyTorch hackathon submission. Basically I am showing that how PDEs on graphs can be solved with Message Passing class of torch_geometric and created a tool torch_pdegraph to facilitate this. I work in the field of PDEs on graphs and I realized that Message passagin equation (the way of forward convolution) on graphs (https://arxiv.org/pdf/1704.01212.pdf) can also be used to solve some PDEs on graphs.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.01212/"&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>
