9 citations · 18 across the 4 of their papers we have counts for
4 papers
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Philippe Wenk, Alkis Gotovos, Stefan Bauer +3
Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data ha…
Scalable Variational Inference for Dynamical Systems
Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
Gradient matching is a promising tool for learning parameters and state dynamics of ordinary differential equations. It is a grid free inference approach, which, for fully observab…
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
Gradient matching with Gaussian processes is a promising tool for learning parameters of ordinary differential equations (ODE's). The essence of gradient matching is to model the p…
Model Selection for Gaussian Process Regression by Approximation Set Coding
Benjamin Fischer, Nico Gorbach, Stefan Bauer +2
Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel…