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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1707.01926/</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/1707.01926/feed.xml" rel="self" type="application/rss+xml" /><item><title>Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting — mentioned by dave-anderson</title><link>https://news.ycombinator.com/item?id=21093405</link><guid isPermaLink="false">urn:hackerpapers:mention:1707.01926:21093405</guid><pubDate>Fri, 27 Sep 2019 16:02:55 GMT</pubDate><description>&lt;p&gt;The paper has no problem there at all. Many prior papers have demonstrated that neural network models significantly outperform non-neural network models in transportation forecasting like https://arxiv.org/abs/1707.01926, http://www-scf.usc.edu/~yaguang/papers/aaai19_multi_graph_co... As this paper has chosen the state-of-the-art neural network models as baselines, it has already done enough and decent jobs on choosing baselines.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1707.01926/"&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>
