21 citations · 41 across the 4 of their papers we have counts for
3 papers · 1 filter
Identifiable Feature Learning for Spatial Data with Nonlinear ICA
Hermanni Hälvä, Jonathan So, Richard E. Turner +1
Recently, nonlinear ICA has surfaced as a popular alternative to the many heuristic models used in deep representation learning and disentanglement. An advantage of nonlinear ICA i…
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy +4
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). Our contribution is to…
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
Hermanni Hälvä, Aapo Hyvärinen
Recent advances in nonlinear Independent Component Analysis (ICA) provide a principled framework for unsupervised feature learning and disentanglement. The central idea in such wor…