ICE-BeeM: IdentifiableConditionalEnergy-Based DeepModelsBasedonNonlinearICA

Our results extend recent developments innonlinear ICA, and in fact, they lead to an important generalization of ICA models. In particular, we show that our model can be used for the estimation of the components in theframeworkofIndependentlyModulatedComponentAnalysis(IMCA),anew generalization of nonlinear ICA that relaxes the independence assumption.