<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Poisson Flow Generative Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2209.11178/</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/2209.11178/feed.xml" rel="self" type="application/rss+xml" /><item><title>Poisson Flow Generative Models — mentioned by c1ccccc1</title><link>https://news.ycombinator.com/item?id=33809929</link><guid isPermaLink="false">urn:hackerpapers:mention:2209.11178:33809929</guid><pubDate>Wed, 30 Nov 2022 23:57:41 GMT</pubDate><description>&lt;p&gt;Paper link: https://arxiv.org/pdf/2209.11178.pdf Lots of interesting things I&amp;#x27;d want to try here. Eg, the formulation of thinking of your probability distribution as a charge distribution opens up the possibility of adding negative charges. So you could have a regular positive data examples, but also some negative examples that the generative model tries extra hard to avoid. I&amp;#x27;d also be interested to see what happens when you &amp;quot;add the fields&amp;quot; for two different distributions over the same space. Seems like it would be interestingly different than regular superposition, and I&amp;#x27;d really like to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2209.11178/"&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>
