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- W4384263465 abstract "Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables. Causal representation learning (CRL), however, argues that factors of variation in a dataset are, in fact, causally related. Allowing latent variables to be correlated, as a consequence of causal relationships, is more realistic and generalisable. So far, provably identifiable methods rely on: auxiliary information, weak labels, and interventional or even counterfactual data. Inspired by causal discovery with functional causal models, we propose a fully unsupervised representation learning method that considers a data generation process with a latent additive noise model (ANM). We encourage the latent space to follow a causal ordering via loss function based on the Hessian of the latent distribution." @default.
- W4384263465 created "2023-07-14" @default.
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- W4384263465 date "2023-07-11" @default.
- W4384263465 modified "2023-09-25" @default.
- W4384263465 title "A Causal Ordering Prior for Unsupervised Representation Learning" @default.
- W4384263465 doi "https://doi.org/10.48550/arxiv.2307.05704" @default.
- W4384263465 hasPublicationYear "2023" @default.
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