May 10, 2024, 4:42 a.m. | Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu

cs.LG updates on

arXiv:2306.01213v3 Announce Type: replace
Abstract: Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. We model causal mechanisms using nonlinear learnable flow-based diffeomorphic functions to map noise variables to latent …

abstract arxiv attention causal cs.lg independent information notion perspective tasks type via work

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