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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reconstructing Training Data from Trained Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2206.07758/</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/2206.07758/feed.xml" rel="self" type="application/rss+xml" /><item><title>Reconstructing Training Data from Trained Neural Networks — mentioned by CGamesPlay</title><link>https://news.ycombinator.com/item?id=38401904</link><guid isPermaLink="false">urn:hackerpapers:mention:2206.07758:38401904</guid><pubDate>Fri, 24 Nov 2023 09:02:50 GMT</pubDate><description>&lt;p&gt;You use more math soup that gets the original examples out. Not perfectly, but possibly good enough. Reconstructing Training Data from Trained Neural Networks https://arxiv.org/abs/2206.07758&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2206.07758/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Reconstructing Training Data from Trained Neural Networks — HN submission</title><link>https://news.ycombinator.com/item?id=31822671</link><guid isPermaLink="false">urn:hackerpapers:submission:2206.07758:31822671</guid><pubDate>Tue, 21 Jun 2022 11:28:13 GMT</pubDate><description>&lt;p&gt;Reconstructing Training Data from Trained Neural Networks&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2206.07758/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
