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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Attentive Explanations: Justifying Decisions and Pointing to the Evidence · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1612.04757/</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/1612.04757/feed.xml" rel="self" type="application/rss+xml" /><item><title>Attentive Explanations: Justifying Decisions and Pointing to the Evidence — mentioned by deepnotderp</title><link>https://news.ycombinator.com/item?id=13600694</link><guid isPermaLink="false">urn:hackerpapers:mention:1612.04757:13600694</guid><pubDate>Wed, 08 Feb 2017 18:30:00 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;m not sure I fully buy the &amp;quot;explainability&amp;quot; argument against deep learning since our goal is human level intelligence and human intelligence isn&amp;#x27;t easily explained either. On the other hand, we&amp;#x27;re nowhere near human level intelligence in most tasks (Go, Poker and image classificaiton non withholding) so I can understand the argument for explainability from a practical perspective. I think we&amp;#x27;re making some good progress in that direction though, and I&amp;#x27;ll list them below: 1) Attention maps in CNNs can tell us what the net is usually looking at. 2) &amp;quot;Attentive Explanations&amp;quot; use attention…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1612.04757/"&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>
