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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2306.02572/</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/2306.02572/feed.xml" rel="self" type="application/rss+xml" /><item><title>Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence — mentioned by sorobahn</title><link>https://news.ycombinator.com/item?id=40686866</link><guid isPermaLink="false">urn:hackerpapers:mention:2306.02572:40686866</guid><pubDate>Sat, 15 Jun 2024 01:49:05 GMT</pubDate><description>&lt;p&gt;I feel like this is a really hard problem to solve generally and there are smart researchers like Yann LeCun trying to figure out the role of search in creating AGI. Yann&amp;#x27;s current bet seems to be on Joint Embedding Predictive Architectures (JEPA) for representation learning to eventually build a solid world model where the agent can test theories by trying different actions (aka search). I think this paper [0] does a good job in laying out his potential vision, but it is all ofc harder than just search + transformers. There is an assumption that language is good enough at representing our…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2306.02572/"&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>
