Causality 101 with Robert Osazuwa Ness - #342
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Understanding Causal Mechanisms in Embedding Spaces
This chapter discusses disentangled representations and variational autoencoders, highlighting their connections to latent causes in causal inference. It examines the invariance of conditional probability distributions across datasets and emphasizes the role of embedding space in enhancing the clarity of causal representations.
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