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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Online Embedding Compression for Text Classification using Low Rank Matrix Factorization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1811.00641/</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/1811.00641/feed.xml" rel="self" type="application/rss+xml" /><item><title>Online Embedding Compression for Text Classification using Low Rank Matrix Factorization — mentioned by anishacharya</title><link>https://news.ycombinator.com/item?id=18918085</link><guid isPermaLink="false">urn:hackerpapers:mention:1811.00641:18918085</guid><pubDate>Wed, 16 Jan 2019 03:47:21 GMT</pubDate><description>&lt;p&gt;The paper is here: https://arxiv.org/pdf/1811.00641.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1811.00641/"&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>Online Embedding Compression for Text Classification using Low Rank Matrix Factorization — mentioned by azhenley</title><link>https://news.ycombinator.com/item?id=18913480</link><guid isPermaLink="false">urn:hackerpapers:mention:1811.00641:18913480</guid><pubDate>Tue, 15 Jan 2019 18:21:58 GMT</pubDate><description>&lt;p&gt;The article&amp;#x27;s title is actually &amp;quot;New Method for Compressing Neural Networks Better Preserves Accuracy&amp;quot;. Summary of a key result: &amp;quot;In one set of experiments, for instance, we showed that our system could shrink a neural network by 90 percent while reducing its accuracy by less than 1%. At the same compression rate, the best prior method reduced the accuracy by about 3.5%.&amp;quot; Link to the 2019 AAAI paper: https://arxiv.org/pdf/1811.00641.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1811.00641/"&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>
