2min chapter

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) cover image

Weakly Supervised Causal Representation Learning with Johann Brehmer - #605

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

CHAPTER

The Latent Causal Model Theorem

Theory says if you have two models and then a map that takes these causal variables and maps them to some data space so think pixels. So we call this a latent causal model. Now what the theorem says is both of these latent causal models give rise to the same kind of data setif you look at them. There's the same distribution. The hidden assumption here is that indeed nature operates as such a causal model, but sometimes our assumptions are not satisfied.

00:00

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.
App store bannerPlay store banner

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export

Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode