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Found 6 papers, 4 papers with code
Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning
A central problem in unsupervised deep learning is how to find useful representations of high-dimensional data, sometimes called "disentanglement".
Identifiability of latent-variable and structural-equation models: from linear to nonlinear
An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable, i. e. some parameters cannot be uniquely estimated.
Causal Autoregressive Flows
We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging from causal discovery to making interventional and counterfactual predictions.
Autoregressive flow-based causal discovery and inference
We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions.
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional energy-based models are unique in function space, up to a simple transformation.
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement.
