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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Towards a Better Understanding of Predict and Count Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1511.02024/</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/1511.02024/feed.xml" rel="self" type="application/rss+xml" /><item><title>Towards a Better Understanding of Predict and Count Models — mentioned by cschmidt</title><link>https://news.ycombinator.com/item?id=13774364</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.02024:13774364</guid><pubDate>Thu, 02 Mar 2017 14:54:44 GMT</pubDate><description>&lt;p&gt;The paper you link is about word2vec, which has taken quite a while to understand. This paper shows word2vec is a particular factorization of the word-context matrix. Neural Word Embedding as Implicit Matrix Factorization https://papers.nips.cc/paper/5477-neural-word-embedding-as-i... Which is some of the story. I found this paper also interesting. Towards a Better Understanding of Predict and Count Models https://arxiv.org/abs/1511.02024&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.02024/"&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>
