PDGMM-VAE recovers latent sources in nonlinear ICA by using jointly learned per-dimension GMM priors that fit source-specific marginals and reduce permutation symmetry.
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A nonlinear ICA method is derived to estimate general quadratic noise coupling and tested on simulated data plus real KAGRA gravitational wave strain.
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PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA
PDGMM-VAE recovers latent sources in nonlinear ICA by using jointly learned per-dimension GMM priors that fit source-specific marginals and reduce permutation symmetry.
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Nonlinear Independent Component Analysis Scheme and its application to gravitational wave data analysis
A nonlinear ICA method is derived to estimate general quadratic noise coupling and tested on simulated data plus real KAGRA gravitational wave strain.