<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Architectural Backdoors in Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2206.07840/</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/2206.07840/feed.xml" rel="self" type="application/rss+xml" /><item><title>Architectural Backdoors in Neural Networks — mentioned by arnaudsm</title><link>https://news.ycombinator.com/item?id=35315921</link><guid isPermaLink="false">urn:hackerpapers:mention:2206.07840:35315921</guid><pubDate>Sun, 26 Mar 2023 16:00:35 GMT</pubDate><description>&lt;p&gt;Weights can be retrained but cannot be explained. Recent papers even demonstrated backdoors : https://arxiv.org/abs/2206.07840 Therefore a real open source model needs a public dataset and training code.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2206.07840/"&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>
