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Found 5 papers, 2 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.
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).
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
The central idea in such works is that the latent components are assumed to be independent conditional on some observed auxiliary variables, such as the time-segment index.
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process
Additivity greatly limits the generality of the model, hindering analysis of general NVAR processes which have nonlinear interactions between the innovations.
Computational Graph Approach for Detection of Composite Human Activities
The resulting model learns 1) a representation of the atomic activities of a sport and 2) to classify physical activities as compositions of the atomic activities.
