32 citations · 91 across the 11 of their papers we have counts for
4 papers · 1 filter
Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström +2
In this paper, we propose Lagrangian Gaussian Processes (LGPs) for probabilistic and data-efficient learning of dynamics via discrete forced Euler-Lagrange equations. Importantly,…
Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
Jan-Hendrik Ewering, Robin E. Herrmann, Niklas Wahlström +2
Embedding non-restrictive prior knowledge, such as energy conservation laws, into learning methods is a key motive to construct physically consistent dynamics models from limited d…
Invertible Kernel PCA with Random Fourier Features
Daniel Gedon, Antôni H. Ribeiro, Niklas Wahlström +1
Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to…
Learning deep autoregressive models for hierarchical data
Carl R. Andersson, Niklas Wahlström, Thomas B. Schön
We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hie…