1 code implementation • 31 Mar 2020 • Daniel Gedon, Niklas Wahlström, Thomas B. Schön, Lennart Ljung
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs.
1 code implementation • 4 Sep 2019 • Carl Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön
Recent developments within deep learning are relevant for nonlinear system identification problems.
1 code implementation • 9 Mar 2023 • Daniel Gedon, Antôni H. Ribeiro, Niklas Wahlström, Thomas B. Schön
Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation.
1 code implementation • 28 Nov 2022 • Philipp Pilar, Niklas Wahlström
Physics-informed neural networks (PINNs) constitute a flexible approach to both finding solutions and identifying parameters of partial differential equations.
1 code implementation • 3 Feb 2022 • Philipp Pilar, Carl Jidling, Thomas B. Schön, Niklas Wahlström
Machine learning models can be improved by adapting them to respect existing background knowledge.
no code implementations • 15 Sep 2015 • Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, Simo Särkkä
Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation.
no code implementations • 25 Jan 2018 • Carl Andersson, Niklas Wahlström, Thomas B. Schön
We consider the problem of impulse response estimation of stable linear single-input single-output systems.
no code implementations • NeurIPS 2017 • Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön
We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints.
no code implementations • 8 Oct 2015 • John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ("torques") from pixel information only.
Model-based Reinforcement Learning Model Predictive Control +2
no code implementations • 8 Feb 2015 • Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only.
Model-based Reinforcement Learning Model Predictive Control +2
no code implementations • 28 Oct 2014 • Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
In particular, we jointly learn a low-dimensional embedding of the observation by means of deep auto-encoders and a predictive transition model in this low-dimensional space.
no code implementations • 11 Sep 2018 • Zenith Purisha, Carl Jidling, Niklas Wahlström, Simo Särkkä, Thomas B. Schön
The approach also allows for reformulation of come classical regularization methods as Laplacian and Tikhonov regularization as Gaussian process regression, and hence provides an efficient algorithm and principled means for their parameter tuning.
no code implementations • 28 Apr 2021 • 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.
no code implementations • 19 Jun 2023 • Philipp Pilar, Niklas Wahlström
While generated samples often are indistinguishable from real data, mode-collapse may occur and there is no guarantee that they will follow the true data distribution.