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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dataset Distillation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1811.10959/</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/1811.10959/feed.xml" rel="self" type="application/rss+xml" /><item><title>Dataset Distillation — mentioned by gota</title><link>https://news.ycombinator.com/item?id=27967645</link><guid isPermaLink="false">urn:hackerpapers:mention:1811.10959:27967645</guid><pubDate>Tue, 27 Jul 2021 01:39:20 GMT</pubDate><description>&lt;p&gt;I guess I can see some parallel the way you describe it, but it is not that tight of an analogy IMO. Correct me if I&amp;#x27;m wrong but the Lottery Ticket Hypothesis mainly means that, for many of our tasks, we start with needlessly huge networks, and thus a good portion of the learning process is just the network learning to &amp;quot;cope with the noise&amp;quot; from the extraneous connections Hence why the lottery ticket network (being a smaller version of the same initial network) can achieve similar accuracy (but not &amp;#x27;better&amp;#x27;) in much shorter time - because you took away all the confounding stuff. That kind of…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1811.10959/"&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>
