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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning with Requirements: a Manifesto · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2304.03674/</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/2304.03674/feed.xml" rel="self" type="application/rss+xml" /><item><title>Machine Learning with Requirements: a Manifesto — mentioned by hcarlens</title><link>https://news.ycombinator.com/item?id=38772142</link><guid isPermaLink="false">urn:hackerpapers:mention:2304.03674:38772142</guid><pubDate>Tue, 26 Dec 2023 14:41:28 GMT</pubDate><description>&lt;p&gt;An interesting approach I came across at NeurIPS a few weeks ago is called &amp;quot;ML with Requirements&amp;quot;[1]: https://arxiv.org/abs/2304.03674 My basic understanding is that it combines &amp;quot;standard&amp;quot; supervised learning techniques (neural nets + SGD) with a set of logical requirements (e.g. in the case of annotating autonomous driving data, things like &amp;quot;a traffic light cannot be red and green at the same time&amp;quot;). The logical requirements not only make the solution more practically useful, but can also help it learn the &amp;quot;right&amp;quot; solution with less labelled data. [1] I don&amp;#x27;t know if they had a NeurIPS paper…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2304.03674/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
