<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1606.00776/</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/1606.00776/feed.xml" rel="self" type="application/rss+xml" /><item><title>Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation — mentioned by transcranial</title><link>https://news.ycombinator.com/item?id=12095348</link><guid isPermaLink="false">urn:hackerpapers:mention:1606.00776:12095348</guid><pubDate>Thu, 14 Jul 2016 16:45:07 GMT</pubDate><description>&lt;p&gt;The problem is that the latent space of a model trained on all comments is still massive. For any sort of coherence, you need to condition the probabilities on smaller spaces, or enforce structural bounds. Here&amp;#x27;s an example of a paper where they simultaneously optimize high-level semantics with token-level sequence probabilities: http://arxiv.org/pdf/1606.00776v2.pdf.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1606.00776/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
