eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling

Understand

State-space graphical models and the variational autoencoder framework provide a principled apparatus for learning dynamical systems from data.

• State-of-the-art probabilistic approaches are often able to scale to large problems at the cost of flexibility of the variational posterior or expressivity of the dynamics model.
• However, those consolidations can be detrimental if the ultimate goal is to learn a generative model capable of explaining the spatiotemporal structure of the data and making accurate forecasts.
• We introduce a low-rank structured variational autoencoding framework for nonlinear Gaussian state-space graphical models capable of capturing dense covariance structures that are important for learning dynamical systems with predictive capabilities.