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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Certified Adversarial Robustness via Randomized Smoothing · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1902.02918/</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/1902.02918/feed.xml" rel="self" type="application/rss+xml" /><item><title>Certified Adversarial Robustness via Randomized Smoothing — mentioned by orange3xchicken</title><link>https://news.ycombinator.com/item?id=28088471</link><guid isPermaLink="false">urn:hackerpapers:mention:1902.02918:28088471</guid><pubDate>Fri, 06 Aug 2021 15:28:42 GMT</pubDate><description>&lt;p&gt;It turns out that a similar technique, where you basically apply noise multiple times to a single image, and average predictions over all noisy images- equivalent to convolving your nn with Gaussian noise yields near state of the art bounds on provable robustness (under a specific class of attacks). The issue is the magnitude of noise you need in order to get practically robust networks is quite large relative to the data you are dealing with. https://arxiv.org/abs/1902.02918 https://arxiv.org/abs/1906.04584&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1902.02918/"&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>
