7 citations · 8 across the 2 of their papers we have counts for
2 papers
stat.ML2025★ 1 cited
Identifiable Autoregressive Variational Autoencoders for Nonlinear and Nonstationary Spatio-Temporal Blind Source Separation
Mika Sipilä, Klaus Nordhausen, Sara Taskinen
The modeling and prediction of multivariate spatio-temporal data involve numerous challenges. Dimension reduction methods can significantly simplify this process, provided that the…
stat.ME2024★ 7 cited
Modelling multivariate spatio-temporal data with identifiable variational autoencoders
Mika Sipilä, Claudia Cappello, Sandra De Iaco +2
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated fro…