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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>On the non-efficient PAC learnability of conjunctive queries · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2208.10255/</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/2208.10255/feed.xml" rel="self" type="application/rss+xml" /><item><title>On the non-efficient PAC learnability of conjunctive queries — mentioned by nyrikki</title><link>https://news.ycombinator.com/item?id=37772041</link><guid isPermaLink="false">urn:hackerpapers:mention:2208.10255:37772041</guid><pubDate>Wed, 04 Oct 2023 21:27:04 GMT</pubDate><description>&lt;p&gt;The halting problem simply doesn&amp;#x27;t apply to biological neurons, they don&amp;#x27;t side step them, unless your context is humans writing down an Algorithm. The rules of a human writing down an algorithm is the same thing as a Turing machine running an algorithm. The halting problem applies for any system of computation that is at least as powerful as a TM, including any type of arithmetic or non-arithmetic calculation that is well-defined, AKA deterministic. Cortical neuron firing is non-deterministic and more closely is modeled as probabilistic but still stochastic.…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2208.10255/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DB</category></item></channel></rss>
