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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning to Reason With Adaptive Computation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1610.07647/</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/1610.07647/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning to Reason With Adaptive Computation — mentioned by ninjin</title><link>https://news.ycombinator.com/item?id=14302632</link><guid isPermaLink="false">urn:hackerpapers:mention:1610.07647:14302632</guid><pubDate>Tue, 09 May 2017 18:28:01 GMT</pubDate><description>&lt;p&gt;One of my students tried something along these lines for Natural Language Inference (NLI) last year. [1] The results where not conclusive, but perhaps Machine Translation is a better target? My reason for believing this is that the specific dataset for NLI most likely does not require multiple steps of inference for most cases (you can get away with simple token overlap), while the decoder in MT does so since it is constrained to output a single token at each step. [1]: https://arxiv.org/abs/1610.07647&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1610.07647/"&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>
