no code implementations • 28 Nov 2023 • Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
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.
1 code implementation • 18 Oct 2023 • Jonathan So, Richard E. Turner
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.
1 code implementation • NeurIPS 2021 • Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvarinen
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA).
1 code implementation • 20 Oct 2020 • Matthew Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering.