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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Metric Space Magnitude and Generalisation in Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2305.05611/</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/2305.05611/feed.xml" rel="self" type="application/rss+xml" /><item><title>Metric Space Magnitude and Generalisation in Neural Networks — mentioned by Pseudomanifold</title><link>https://news.ycombinator.com/item?id=36844084</link><guid isPermaLink="false">urn:hackerpapers:mention:2305.05611:36844084</guid><pubDate>Mon, 24 Jul 2023 07:13:15 GMT</pubDate><description>&lt;p&gt;Amazing to see &amp;#x27;magnitude&amp;#x27; on the front page of Hacker News! If you are interested in a direct application of this invariant beyond ecology, check out our recent pre-print in which we study the generalisation behaviour of neural networks: https://arxiv.org/abs/2305.05611 My personal approach to magnitude is not based on category theory but rather based on weightings of a metric space. If your metric satisfies certain properties, you can obtain a measure of the &amp;#x27;effective number of points&amp;#x27; of a metric space. This is particularly relevant when looking at the metric space from different…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2305.05611/"&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>
