Difference between revisions of "GP SSM"

From Robotics
Jump to: navigation, search
m (Papers on GP-SSMs)
m (Papers on GP-SSMs)
Line 31: Line 31:
 
* A. Marco, P. Hennig, S. Schaal, S. Trimpe, [[Media:DesignLQRKernels.pdf | On the Design of LQR Kernels for Efficient Controller Learning]], ''arXiv:1709.07089v1''
 
* A. Marco, P. Hennig, S. Schaal, S. Trimpe, [[Media:DesignLQRKernels.pdf | On the Design of LQR Kernels for Efficient Controller Learning]], ''arXiv:1709.07089v1''
 
* N. Gorbach, S. Bauer, J. Buhmann, [[Media:ScalableVariationalInference.pdf | Scalable Variational Inference for Dynamical Systems]], NIPS 2017, Long Beach, CA, 2017.
 
* N. Gorbach, S. Bauer, J. Buhmann, [[Media:ScalableVariationalInference.pdf | Scalable Variational Inference for Dynamical Systems]], NIPS 2017, Long Beach, CA, 2017.
 +
* J. Umlauft, T. Beckers, M. Kimmel, S. Hirsche, [[Media:FeedbackLinearlizatingUsingGPs.pdf | Feedback Linearization Using Gaussian Processes]]
  
 
=== Web Links ===
 
=== Web Links ===
 
* [http://dsc.ijs.si/jus.kocijan/GPdyn/ Bibliography on GP Models in Dynamical Systems]
 
* [http://dsc.ijs.si/jus.kocijan/GPdyn/ Bibliography on GP Models in Dynamical Systems]

Revision as of 01:30, 8 January 2018

This page gathers references and materials related to the study of "Gaussian Process (GP) State Space Models (SSM)."

Basic Gaussian Process Info

  • Rasmussen and Williams

Papers on GP-SSMs


Web Links