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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1904.12584/</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/1904.12584/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision — mentioned by taliesinb</title><link>https://news.ycombinator.com/item?id=20072580</link><guid isPermaLink="false">urn:hackerpapers:mention:1904.12584:20072580</guid><pubDate>Sun, 02 Jun 2019 00:02:42 GMT</pubDate><description>&lt;p&gt;What excites me about this and similar work (e.g. https://arxiv.org/abs/1904.12584) is that it augurs well for a new era of AI in which we combine the strength of deep learning (specifically, the teaching signal provided by automatic differentiation) and symbolic GOFAI (with its appeal to interpretability and traditional engineering principles). Of course we need to keep the gradients, architecture search, hyperparameter tuning, scalable training to massive datasets, etc., but there is a growing sense that the programs we write can encode extremely powerful priors about how the world works…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1904.12584/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
