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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Exploring Interpretable LSTM Neural Networks over Multi-Variable Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1905.12034/</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/1905.12034/feed.xml" rel="self" type="application/rss+xml" /><item><title>Exploring Interpretable LSTM Neural Networks over Multi-Variable Data — mentioned by tand22</title><link>https://news.ycombinator.com/item?id=33444268</link><guid isPermaLink="false">urn:hackerpapers:mention:1905.12034:33444268</guid><pubDate>Wed, 02 Nov 2022 23:25:59 GMT</pubDate><description>&lt;p&gt;I implemented an interpretable LSTM from this paper https://arxiv.org/pdf/1905.12034.pdf. It works by segmenting the context for each input variable, and then applying an attention layer to interpret each variables effect. I applied it to mortality prediction based on ICU data, the prediction/recall was pretty good considering the small size of the dataset used.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1905.12034/"&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>
