Mechanistic independence criteria yield identifiability of latent subspaces under nonlinear mixing by focusing on action-based independence rather than latent distributions, with a hierarchy and graph-theoretic view of subspaces.
Identifia- bility results for multimodal contrastive learning.arXiv preprint arXiv:2303.09166,
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Mechanistic Independence: A Principle for Identifiable Disentangled Representations
Mechanistic independence criteria yield identifiability of latent subspaces under nonlinear mixing by focusing on action-based independence rather than latent distributions, with a hierarchy and graph-theoretic view of subspaces.
- Understanding Self-Supervised Learning via Latent Distribution Matching