Search Results for author: Niklas Wahlström

Found 12 papers, 4 papers with code

Physics-informed neural networks with unknown measurement noise

1 code implementation28 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.

Incorporating Sum Constraints into Multitask Gaussian Processes

1 code implementation3 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.

Gaussian Processes

Learning deep autoregressive models for hierarchical data

no code implementations28 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.

Deep State Space Models for Nonlinear System Identification

1 code implementation31 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.

Deep Convolutional Networks in System Identification

1 code implementation4 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.

Probabilistic approach to limited-data computed tomography reconstruction

no code implementations11 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.

Numerical Integration

Data-Driven Impulse Response Regularization via Deep Learning

no code implementations25 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.

Linearly constrained Gaussian processes

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.

Gaussian Processes

Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

no code implementations8 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 reinforcement-learning

From Pixels to Torques: Policy Learning with Deep Dynamical Models

no code implementations8 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 reinforcement-learning

Learning deep dynamical models from image pixels

no code implementations28 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.