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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Program Synthesis with Priority Queue Training · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1801.03526/</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/1801.03526/feed.xml" rel="self" type="application/rss+xml" /><item><title>Neural Program Synthesis with Priority Queue Training — mentioned by xkapastel</title><link>https://news.ycombinator.com/item?id=19207583</link><guid isPermaLink="false">urn:hackerpapers:mention:1801.03526:19207583</guid><pubDate>Wed, 20 Feb 2019 13:25:09 GMT</pubDate><description>&lt;p&gt;&amp;gt; I wonder what the result would look like if this were applied to source code. IMHO a probabilistic algorithm for code completion could turn out interesting. Neural program synthesis is similar to what you describe, here&amp;#x27;s a sample paper: &amp;gt; We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1801.03526/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
