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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Representation Theory Perspective on Simultaneous Alignment and Classification · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1607.03464/</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/1607.03464/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Representation Theory Perspective on Simultaneous Alignment and Classification — mentioned by Xcelerate</title><link>https://news.ycombinator.com/item?id=14128768</link><guid isPermaLink="false">urn:hackerpapers:mention:1607.03464:14128768</guid><pubDate>Mon, 17 Apr 2017 01:58:13 GMT</pubDate><description>&lt;p&gt;&amp;gt; My bet is on micro lenses so small that the camera becomes a flat rectangle taking up most of one side of the phone, along with some crazy insane software processing. As someone who does research on inverse signal recovery, what can be recovered nowadays is absolutely mindblowing. I work on the group synchronization problem, which aims to extract an underlying ground truth signal from noisy, shifted copies of that signal. In my case, I am extracting atomic environments from noisy rotated and permuted copies of an &amp;quot;archetypal&amp;quot; atomic environment, but I imagine the same technique applied to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1607.03464/"&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>
