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Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
Jun Wei Ng, Marc Peter Deisenroth
We propose a practical and scalable Gaussian process model for large-scale nonlinear probabilistic regression. Our mixture-of-experts model is conceptually simple and hierarchicall…
Learning deep dynamical models from image pixels
Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only ava…
Manifold Gaussian Processes for Regression
Roberto Calandra, Jan Peters, Carl Edward Rasmussen +1
Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable funct…