Deep latent Gaussian models (DLGMs) composed of density and inference networks [14]—the pipeline that defines a Variational Autoencoder [8]—have achieved notable success on tasks ranging from image modeling [3] to semi-supervised classification [6, 11]. However, the approximate posterior in these models is usually chosen to be a factorized Gaussian, thereby imposing strong constraints on the po...

The Cluster-Weighted Modeling (CWM) is emerging as a versatile tool for modeling dynamical systems. It is a mixture density estimator around local models. To be specific, the input regions together with output regions are treated to be Gaussian serving as local models. These models are linked by a linear or non-linear function involving the mixture of densities of local models. The present work...