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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>On Tiny Episodic Memories in Continual Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1902.10486/</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/1902.10486/feed.xml" rel="self" type="application/rss+xml" /><item><title>On Tiny Episodic Memories in Continual Learning — mentioned by uomopertica</title><link>https://news.ycombinator.com/item?id=22501782</link><guid isPermaLink="false">urn:hackerpapers:mention:1902.10486:22501782</guid><pubDate>Fri, 06 Mar 2020 08:02:31 GMT</pubDate><description>&lt;p&gt;Machine Learning phd student here! I am currently working on a branch of ML called Continual Learning that deals with this theme exactly. It turns out Artificial Neural Networks have a hard time recalling things they they see in the past [1]. One of the most effective strategies to counter this issue is storing and periodically replaying old training items (i.e. “memories”) for the network [2]. There are also approaches that set out to mimic what we understand of the brain and adopt cue-based approaches [3], but they don’t seem to have reached meaningful outbreaks just yet... [1]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1902.10486/"&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>
