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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Approximating Permutations with Neural Network Components for Travelling Photographer Problem · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2205.00242/</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/2205.00242/feed.xml" rel="self" type="application/rss+xml" /><item><title>Approximating Permutations with Neural Network Components for Travelling Photographer Problem — mentioned by tartakovsky</title><link>https://news.ycombinator.com/item?id=31522996</link><guid isPermaLink="false">urn:hackerpapers:mention:2205.00242:31522996</guid><pubDate>Thu, 26 May 2022 20:29:48 GMT</pubDate><description>&lt;p&gt;Can we learn permutation matrices? Something like this but applied to learning order that maximizes some objective. Off the cuff it sounds like an NP problem but seems that people have tried before with some success. https://arxiv.org/abs/2205.00242v1 It also reminds me of tuning random seeds (can&amp;#x27;t find the arxiv link) Or things like this: https://arxiv.org/abs/2104.06338 Overall this stuff seems highly intractable in today&amp;#x27;s world but the humans are trying to make headway. Such is research.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2205.00242/"&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>
