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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1706.10268/</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/1706.10268/feed.xml" rel="self" type="application/rss+xml" /><item><title>SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud — mentioned by gtani</title><link>https://news.ycombinator.com/item?id=14740877</link><guid isPermaLink="false">urn:hackerpapers:mention:1706.10268:14740877</guid><pubDate>Tue, 11 Jul 2017 01:07:59 GMT</pubDate><description>&lt;p&gt;This seems to be a newly hot topic, whether to fight adversarial inputs or looking for efficiencies. I bookmarked a few relevant blogs (but really need to read more deeply) http://composition.al/blog/2017/05/31/proving-that-safety-cr... http://www.cleverhans.io/security/privacy/ml/2017/06/14/veri... https://blog.foretellix.com/ and another preprint: https://arxiv.org/abs/1706.10268&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1706.10268/"&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>
