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Found 4 papers, 3 papers with code
Identifiable Feature Learning for Spatial Data with Nonlinear ICA
In this paper, we introduce a new nonlinear ICA framework that employs $t$-process (TP) latent components which apply naturally to data with higher-dimensional dependency structures, such as spatial and spatio-temporal data.
Optimising Distributions with Natural Gradient Surrogates
In this work we propose a novel technique for tackling such issues, which involves reframing the optimisation as one with respect to the parameters of a surrogate distribution, for which computing the natural gradient is easy.
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA).
Sparse Gaussian Process Variational Autoencoders
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering.
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