Magnetometers for tracking metallic targets. - Niklas Wahlstrom; Jonas Callmer; Fredrik Gustafsson (FUSION, 2010)

Single target tracking using vector magnetometers. - Niklas Wahlstrom; Jonas Callmer; Fredrik Gustafsson (ICASSP, 2011)

A voyage to Africa by Mr Swift. - Niklas Wahlstrom; Fredrik Gustafsson; Susanne Åkesson (FUSION, 2012)

Rapid classification of vehicle heading direction with two-axis magnetometer. - Niklas Wahlstrom; Roland Hostettler; Fredrik Gustafsson; Wolfgang Birk (ICASSP, 2012)

MEMS-based inertial navigation based on a magnetic field map. - Manon Kok; Niklas Wahlstrom; Thomas B. Schön; Fredrik Gustafsson (ICASSP, 2013)

Modeling magnetic fields using Gaussian processes. - Niklas Wahlstrom; Manon Kok; Thomas B. Schön; Fredrik Gustafsson (ICASSP, 2013)

Robust NLS sensor localization using MDS initialization. - Viktor Deleskog; Hans Habberstad; Gustaf Hendeby; David Lindgren; Niklas Wahlstrom (FUSION, 2014)

Learning deep dynamical models from image pixels. - Niklas Wahlström; Thomas B. Schön; Marc Peter Deisenroth (CoRR, 2014)

Classification of Driving Direction in Traffic Surveillance Using Magnetometers. - Niklas Wahlstrom; Roland Hostettler; Fredrik Gustafsson; Wolfgang Birk (IEEE Trans. Intell. Transp. Syst., 2014)

Magnetometer Modeling and Validation for Tracking Metallic Targets. - Niklas Wahlstrom; Fredrik Gustafsson (IEEE Trans. Signal Process., 2014)

Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models. - John-Alexander M. Assael; Niklas Wahlström; Thomas B. Schön; Marc Peter Deisenroth (CoRR, 2015)

From Pixels to Torques: Policy Learning with Deep Dynamical Models. - Niklas Wahlström; Thomas B. Schön; Marc Peter Deisenroth (CoRR, 2015)

Extended Target Tracking Using Gaussian Processes. - Niklas Wahlstrom; Emre Özkan (IEEE Trans. Signal Process., 2015)

Modeling of Magnetic Fields and Extended Objects for Localization Applications. - Niklas Wahlström (2015)

A bounded confidence model that preserves the signs of the opinions. - Francesca Ceragioli; Gustav Lindmark; Clas Veiback; Niklas Wahlstrom; Martin Lindfors; Claudio Altafini (ECC, 2016)

Rao-Blackwellised particle filter for star-convex extended target tracking models. - Emre Özkan; Niklas Wahlstrom; Simon J. Godsill (FUSION, 2016)

Linearly constrained Gaussian processes. - Carl Jidling; Niklas Wahlström; Adrian Wills; Thomas B. Schön (NIPS, 2017)

Data-Driven Impulse Response Regularization via Deep Learning. - Carl R. Andersson; Niklas Wahlström; Thomas B. Schön (CoRR, 2018)

Probabilistic approach to limited-data computed tomography reconstruction. - Zenith Purisha; Carl Jidling; Niklas Wahlström; Simo Särkkä; Thomas B. Schön (CoRR, 2018)

Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes. - Arno Solin; Manon Kok; Niklas Wahlstrom; Thomas B. Schön; Simo Särkkä (IEEE Trans. Robotics, 2018)

Deep Convolutional Networks in System Identification. - Carl R. Andersson; Antônio H. Ribeiro; Koen Tiels; Niklas Wahlström; Thomas B. Schön (CDC, 2019)

Deep State Space Models for Nonlinear System Identification. - Daniel Gedon; Niklas Wahlström; Thomas B. Schön; Lennart Ljung (CoRR, 2020)

First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG. - Daniel Gedon; Antônio H. Ribeiro; Niklas Wahlström; Thomas B. Schön (CinC, 2021)

Learning deep autoregressive models for hierarchical data. - Carl R. Andersson; Niklas Wahlström; Thomas B. Schön (CoRR, 2021)

Incorporating Sum Constraints into Multitask Gaussian Processes. - Philipp Pilar; Carl Jidling; Thomas B. Schön; Niklas Wahlström (Trans. Mach. Learn. Res., 2022)

Invertible Kernel PCA With Random Fourier Features. - Daniel Gedon; Antônio H. Ribeiro; Niklas Wahlström; Thomas B. Schön (IEEE Signal Process. Lett., 2023)

Physics-informed neural networks with unknown measurement noise. - Philipp Pilar; Niklas Wahlström (L4DC, 2024)

Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks. - Philipp Pilar; Niklas Wahlström (Trans. Mach. Learn. Res., 2024)

Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks. - Philipp Pilar; Markus Heinonen; Niklas Wahlström (CoRR, 2025)