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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generating Visual Explanations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1603.08507/</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/1603.08507/feed.xml" rel="self" type="application/rss+xml" /><item><title>Generating Visual Explanations — mentioned by yoav_hollander</title><link>https://news.ycombinator.com/item?id=12451675</link><guid isPermaLink="false">urn:hackerpapers:mention:1603.08507:12451675</guid><pubDate>Thu, 08 Sep 2016 10:13:41 GMT</pubDate><description>&lt;p&gt;Right. This paper ([0]) is actually mentioned in the DARPA BAA ([1]) as an example of a possible direction. A somewhat-similar scheme is [2]. Both seem to do some kind of sensitivity analysis, so as to show the user which parts of the input were most important for coming up with the decision: For instance, [2] &amp;quot;explains&amp;quot; an ML system (which answers questions about pictures), by telling you which pixels were most important for the decision. It does that by essentially hiding pixels and seeing how that influences the ML system&amp;#x27;s decisions. So this produces not so much an explanation as &amp;quot;hints&amp;quot;…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1603.08507/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
