Thomas Schön

Underlätta samarbeten mellan företag och universitet

Strand, Magnus; Wedlin, Linda; Schön, Thomas B.

2025

Automated segmentation of synchrotron-scanned fossils

During, Melanie A. D.; Matelsky, Jordan K.; Gustafsson, Fredrik K.; Voeten, Dennis F. A. E. et al.

Ingår i Fossil Record, s. 103-114, 2025

• DOI för Automated segmentation of synchrotron-scanned fossils
• Ladda ner fulltext (pdf) av Automated segmentation of synchrotron-scanned fossils

Rättsliga hinder mot samverkan?: Juridik och ledningsfrågor vid doktorandsamverkan

Strand, Magnus; Wedlin, Linda; Schön, Thomas B.

2025

Deep networks for system identification: A survey

Pillonetto, Gianluigi; Aravkin, Aleksandr; Gedon, Daniel; Ljung, Lennart et al.

Ingår i Automatica, 2025

• DOI för Deep networks for system identification: A survey
• Ladda ner fulltext (pdf) av Deep networks for system identification: A survey

A Model Predictive Control Approach to Motion Planning in Dynamic Environments

Wullt, Bernhard; Mattsson, Per; Schön, Thomas B.; Norrlöf, Mikael

Ingår i 2024 European Control Conference (ECC), s. 3247-3254, 2024

• DOI för A Model Predictive Control Approach to Motion Planning in Dynamic Environments

Automated segmentation of synchrotron-scanned fossils

During, Melanie A. D.; Matelsky, Jordan K.; Gustafsson, Fredrik K.; Voeten, Dennis F. A. E. et al.

Ingår i Fossil Record, s. 103-114, 2025

• DOI för Automated segmentation of synchrotron-scanned fossils
• Ladda ner fulltext (pdf) av Automated segmentation of synchrotron-scanned fossils

Deep networks for system identification: A survey

Pillonetto, Gianluigi; Aravkin, Aleksandr; Gedon, Daniel; Ljung, Lennart et al.

Ingår i Automatica, 2025

• DOI för Deep networks for system identification: A survey
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Uncertainty Estimation with Recursive Feature Machines

Gedon, Daniel; Abedsoltan, Amirhesam; Schön, Thomas B.; Belkin, Mikhail

Ingår i Proceedings of Machine Learning Research, s. 1408-1437, 2024

Rao-Blackwellized particle smoothing for simultaneous localization and mapping

Kok, Manon; Solin, Arno; Schön, Thomas B.

Ingår i DATA-CENTRIC ENGINEERING, 2024

• DOI för Rao-Blackwellized particle smoothing for simultaneous localization and mapping
• Ladda ner fulltext (pdf) av Rao-Blackwellized particle smoothing for simultaneous localization and mapping

On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches

Mattsson, Per; Bonassi, Fabio; Breschi, Valentina; Schön, Thomas B.

Ingår i IEEE Control Systems Letters, s. 796-801, 2024

• DOI för On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches

Structured state-space models are deep Wiener modelsStructured state-space models are deep Wiener models

Bonassi, Fabio; Andersson, Carl; Mattsson, Per; Schön, Thomas B.

Ingår i IFAC-PapersOnLine, s. 247-252, 2024

• DOI för Structured state-space models are deep Wiener modelsStructured state-space models are deep Wiener models

Evaluating regression and probabilistic methods for ECG-based electrolyte prediction

von Bachmann, Philipp; Gedon, Daniel; Gustafsson, Fredrik K.; Ribeiro, Antônio H. et al.

Ingår i Scientific Reports, 2024

• DOI för Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
• Ladda ner fulltext (pdf) av Evaluating regression and probabilistic methods for ECG-based electrolyte prediction

Machine Learning Based Prediction of Imminent ICP Insults During Neurocritical Care of Traumatic Brain Injury

Galos, Peter; Hult, Ludvig; Zachariah, Dave; Lewén, Anders et al.

Ingår i Neurocritical Care, 2024

• DOI för Machine Learning Based Prediction of Imminent ICP Insults During Neurocritical Care of Traumatic Brain Injury
• Ladda ner fulltext (pdf) av Machine Learning Based Prediction of Imminent ICP Insults During Neurocritical Care of Traumatic Brain Injury

Safe Reinforcement Learning in Uncertain Contexts

Baumann, Dominik; Schön, Thomas B.

Ingår i IEEE Transactions on robotics, s. 1828-1841, 2024

• DOI för Safe Reinforcement Learning in Uncertain Contexts

Screening for Chagas disease from the electrocardiogram using a deep neural network

Jidling, Carl; Gedon, Daniel; Schön, Thomas B.; Oliveira, Claudia Di Lorenzo et al.

Ingår i PLoS Neglected Tropical Diseases, 2023

• DOI för Screening for Chagas disease from the electrocardiogram using a deep neural network
• Ladda ner fulltext (pdf) av Screening for Chagas disease from the electrocardiogram using a deep neural network

How Reliable is Your Regression Model’s Uncertainty Under Real-World Distribution Shifts?

Gustafsson, Fredrik K.; Danelljan, Martin; Schön, Thomas B.

Ingår i Transactions on Machine Learning Research, 2023

Variational Elliptical Processes

Bånkestad, Maria; Sjölund, Jens; Taghia, Jalil; Schön, Thomas B.

Ingår i Transactions on Machine Learning Research, 2023

• Ladda ner fulltext (pdf) av Variational Elliptical Processes

Gaussian inference for data-driven state-feedback design of nonlinear systems

Martin, Tim; Schön, Thomas B.; Allgöwer, Frank

Ingår i IFAC-PapersOnLine, s. 4796-4803, 2023

• DOI för Gaussian inference for data-driven state-feedback design of nonlinear systems
• Ladda ner fulltext (pdf) av Gaussian inference for data-driven state-feedback design of nonlinear systems

Neural motion planning in dynamic environments

Wullt, Bernhard; Mattsson, Per; Schön, Thomas B.; Norrlöf, Mikael

Ingår i IFAC-PapersOnLine, s. 10126-10131, 2023

• DOI för Neural motion planning in dynamic environments
• Ladda ner fulltext (pdf) av Neural motion planning in dynamic environments

On the regularization in DeePC

Mattsson, Per; Schön, Thomas B.

Ingår i IFAC-PapersOnLine, s. 625-631, 2023

• DOI för On the regularization in DeePC

On the trade-off between event-based and periodic state estimation under bandwidth constraints

Baumann, Dominik; Schön, Thomas B.

Ingår i IFAC-PapersOnLine, s. 5275-5280, 2023

• DOI för On the trade-off between event-based and periodic state estimation under bandwidth constraints
• Ladda ner fulltext (pdf) av On the trade-off between event-based and periodic state estimation under bandwidth constraints

Invertible Kernel PCA With Random Fourier Features

Gedon, Daniel; Ribeiro, Antônio H.; Wahlström, Niklas; Schön, Thomas B.

Ingår i IEEE Signal Processing Letters, s. 563-567, 2023

• DOI för Invertible Kernel PCA With Random Fourier Features

Online Learning for Prediction via Covariance Fitting: Computation, Performance and Robustness

Osama, Muhammad; Zachariah, Dave; Stoica, Peter; Schön, Thomas B.

Ingår i Transactions on Machine Learning Research, 2023

Inferring the probability distribution over strain tensors in polycrystals from diffraction based measurements

Henningsson, Axel; Wills, Adrian G.; Hall, Stephen A.; Hendriks, Johannes et al.

Ingår i Computer Methods in Applied Mechanics and Engineering, 2023

• DOI för Inferring the probability distribution over strain tensors in polycrystals from diffraction based measurements
• Ladda ner fulltext (pdf) av Inferring the probability distribution over strain tensors in polycrystals from diffraction based measurements

Smoothed State Estimation via Efficient Solution of Linear Equations

Geng, Li-Hui; Wills, Adrian George; Ninness, Brett; Schön, Thomas Bo

Ingår i IEEE Transactions on Automatic Control, s. 5877-5889, 2023

• DOI för Smoothed State Estimation via Efficient Solution of Linear Equations

Overparameterized Linear Regression Under Adversarial Attacks

Horta Ribeiro, Antônio; Schön, Thomas B.

Ingår i IEEE Transactions on Signal Processing, s. 601-614, 2023

• DOI för Overparameterized Linear Regression Under Adversarial Attacks

Variational system identification for nonlinear state-space models

Courts, Jarrad; Wills, Adrian G.; Schön, Thomas B.; Ninness, Brett

Ingår i Automatica, 2023

• DOI för Variational system identification for nonlinear state-space models

Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals

Zhao, Zheng; Särkkä, Simo; Sjölund, Jens; Schön, Thomas B.

Ingår i IEEE Transactions on Signal Processing, s. 461-476, 2023

• DOI för Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals

Predicting Political Violence Using a State-Space Model

Lindholm, Andreas; Hendriks, Johannes; Wills, Adrian; Schön, Thomas B.

Ingår i International Interactions, s. 759-777, 2022

• DOI för Predicting Political Violence Using a State-Space Model

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients

Gustafsson, Stefan; Gedon, Daniel; Lampa, Erik; Horta Ribeiro, Antônio et al.

Ingår i Scientific Reports, 2022

• DOI för Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
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Direct Transmittance Estimation in Heterogeneous Participating Media Using Approximated Taylor Expansions

Jönsson, Daniel; Kronander, Joel; Unger, Jonas; Schön, Thomas B. et al.

Ingår i IEEE Transactions on Visualization and Computer Graphics, s. 2602-2614, 2022

• DOI för Direct Transmittance Estimation in Heterogeneous Participating Media Using Approximated Taylor Expansions

Data to Controller for Nonlinear Systems: An Approximate Solution

Hendriks, Johannes N.; Holdsworth, James R. Z.; Wills, Adrian G.; Schön, Thomas B. et al.

Ingår i IEEE Control Systems Letters, s. 1196-1201, 2022

• DOI för Data to Controller for Nonlinear Systems: An Approximate Solution

Nonlinear System Identification: Learning While Respecting Physical Models Using a Sequential Monte Carlo Method

Wigren, Anna; Wågberg, Johan; Lindsten, Fredrik; Wills, Adrian G. et al.

Ingår i IEEE CONTROL SYSTEMS MAGAZINE, s. 75-102, 2022

• DOI för Nonlinear System Identification: Learning While Respecting Physical Models Using a Sequential Monte Carlo Method

Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters

Rosato, Conor; Devlin, Lee; Beraud, Vincent; Horridge, Paul et al.

Ingår i IEEE Transactions on Signal Processing, s. 3676-3692, 2022

• DOI för Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters

Memory efficient constrained optimization of scanning-beam lithography

Jidling, Carl; Fleming, Andrew J.; Wills, Adrian G.; Schön, Thomas B.

Ingår i Optics Express, s. 20564-20579, 2022

• DOI för Memory efficient constrained optimization of scanning-beam lithography
• Ladda ner fulltext (pdf) av Memory efficient constrained optimization of scanning-beam lithography

Deep neural network-estimated electrocardiographic age as a mortality predictor

Lima, Emilly M.; Horta Ribeiro, Antônio; Paixao, Gabriela M. M.; Horta Ribeiro, Manoel et al.

Ingår i Nature Communications, 2021

• DOI för Deep neural network-estimated electrocardiographic age as a mortality predictor
• Ladda ner fulltext (pdf) av Deep neural network-estimated electrocardiographic age as a mortality predictor

Stochastic quasi-Newton with line-search regularisation

Wills, Adrian G.; Schön, Thomas B.

Ingår i Automatica, 2021

• DOI för Stochastic quasi-Newton with line-search regularisation

Universal probabilistic programming offers a powerful approach to statistical phylogenetics

Ronquist, Fredrik; Kudlicka, Jan; Senderov, Viktor; Borgström, Johannes et al.

Ingår i Communications Biology, 2021

• DOI för Universal probabilistic programming offers a powerful approach to statistical phylogenetics
• Ladda ner fulltext (pdf) av Universal probabilistic programming offers a powerful approach to statistical phylogenetics

Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays

Carlsson, Håkan; Skog, Isaac; Schön, Thomas B.; Jaldén, Joakim

Ingår i IEEE Sensors Journal, s. 19362-19373, 2021

• DOI för Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays
• Ladda ner fulltext (pdf) av Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays

Gaussian Variational State Estimation for Nonlinear State-Space Models

Courts, Jarrad; Wills, Adrian; Schön, Thomas B.

Ingår i IEEE Transactions on Signal Processing, s. 5979-5993, 2021

• DOI för Gaussian Variational State Estimation for Nonlinear State-Space Models

Automatic diagnosis of the 12-lead ECG using a deep neural network

Ribeiro, Antônio H.; Ribeiro, Manoel Horta; Paixao, Gabriela M. M.; Oliveira, Derick M. et al.

Ingår i Nature Communications, 2020

• DOI för Automatic diagnosis of the 12-lead ECG using a deep neural network
• Ladda ner fulltext (pdf) av Automatic diagnosis of the 12-lead ECG using a deep neural network

Machine Learning in Risk Prediction

Sundström, Johan; Schön, Thomas B.

Ingår i Hypertension, s. 1165-1166, 2020

• DOI för Machine Learning in Risk Prediction

Nonlinear Input Design as Optimal Control of a Hamiltonian System

Umenberger, Jack; Schön, Thomas B.

Ingår i IEEE Control Systems Letters, s. 85-90, 2020

• DOI för Nonlinear Input Design as Optimal Control of a Hamiltonian System

Learning Robust LQ-Controllers Using Application Oriented Exploration

Ferizbegovic, Mina; Umenberger, Jack; Hjalmarsson, Hakan; Schön, Thomas B.

Ingår i IEEE Control Systems Letters, s. 19-24, 2020

• DOI för Learning Robust LQ-Controllers Using Application Oriented Exploration

The effect of interventions on COVID-19

Soltesz, Kristian; Gustafsson, Fredrik; Timpka, Toomas; Jaldén, Joakim et al.

Ingår i Nature, 2020

• DOI för The effect of interventions on COVID-19

On the smoothness of nonlinear system identification

Horta Ribeiro, Antônio; Tiels, Koen; Umenberger, Jack; Schön, Thomas B. et al.

Ingår i Automatica, 2020

• DOI för On the smoothness of nonlinear system identification

Smoothing With Couplings of Conditional Particle Filters

Jacob, Pierre E.; Lindsten, Fredrik; Schön, Thomas B.

Ingår i Journal of the American Statistical Association, s. 721-729, 2020

• DOI för Smoothing With Couplings of Conditional Particle Filters

Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models

Dahlin, Johan; Schön, Thomas B.

Ingår i Journal of Statistical Software, s. 1-41, 2019

• DOI för Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models
• Ladda ner fulltext (pdf) av Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models

Data Consistency Approach to Model Validation

Lindholm, Andreas; Zachariah, Dave; Stoica, Peter; Schön, Thomas B.

Ingår i IEEE Access, s. 59788-59796, 2019

• DOI för Data Consistency Approach to Model Validation
• Ladda ner fulltext (pdf) av Data Consistency Approach to Model Validation

Identification of a Duffing oscillator using particle Gibbs with ancestor sampling

Rogers, T J; Schön, Thomas B.; Lindholm, Andreas; Worden, K et al.

Ingår i Journal of Physics, Conference Series, 2019

• DOI för Identification of a Duffing oscillator using particle Gibbs with ancestor sampling
• Ladda ner fulltext (pdf) av Identification of a Duffing oscillator using particle Gibbs with ancestor sampling

Probabilistic approach to limited-data computed tomography reconstruction

Purisha, Zenith; Jidling, Carl; Wahlström, Niklas; Schön, Thomas B. et al.

Ingår i Inverse Problems, 2019

• DOI för Probabilistic approach to limited-data computed tomography reconstruction

A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors

Kok, Manon; Schön, Thomas B.

Ingår i IEEE Signal Processing Letters, s. 1673-1677, 2019

• DOI för A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors

Elements of Sequential Monte Carlo

Naesseth, Christian A.; Lindsten, Fredrik; Schön, Thomas B.

Ingår i FOUNDATIONS AND TRENDS IN MACHINE LEARNING, s. 187-306, 2019

• DOI för Elements of Sequential Monte Carlo

Neutron transmission strain tomography for non-constant stress-free lattice spacing

Hendriks, Johannes N.; Jidling, Carl; Schön, Thomas B.; Wills, Adrian et al.

Ingår i Nuclear Instruments and Methods in Physics Research Section B, s. 64-73, 2019

• DOI för Neutron transmission strain tomography for non-constant stress-free lattice spacing

High-Dimensional Filtering Using Nested Sequential Monte Carlo

Naesseth, Christian A.; Lindsten, Fredrik; Schön, Thomas B.

Ingår i IEEE Transactions on Signal Processing, s. 4177-4188, 2019

• DOI för High-Dimensional Filtering Using Nested Sequential Monte Carlo

On model order priors for Bayesian identification of SISO linear systems

Valenzuela, Patricio E.; Schön, Thomas B.; Rojas, Cristian R.

Ingår i International Journal of Control, s. 1645-1661, 2019

• DOI för On model order priors for Bayesian identification of SISO linear systems

Optimal controller/observer gains of discounted-cost LQG systems

Bijl, Hildo; Schön, Thomas B.

Ingår i Automatica, s. 471-474, 2019

• DOI för Optimal controller/observer gains of discounted-cost LQG systems

Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

Ingår i Mechanical systems and signal processing, s. 915-928, 2018

• DOI för Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

Schön, Thomas B.; Svensson, Andreas; Murray, Lawrence; Lindsten, Fredrik

Ingår i Mechanical systems and signal processing, s. 866-883, 2018

• DOI för Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

Probabilistic modelling and reconstruction of strain

Jidling, Carl; Hendriks, Johannes; Wahlström, Niklas; Gregg, Alexander et al.

Ingår i Nuclear Instruments and Methods in Physics Research Section B, s. 141-155, 2018

• DOI för Probabilistic modelling and reconstruction of strain

Automated learning with a probabilistic programming language: Birch

Murray, Lawrence; Schön, Thomas B.

Ingår i Annual Reviews in Control, s. 29-43, 2018

• DOI för Automated learning with a probabilistic programming language: Birch

Maximum likelihood identification of stable linear dynamical systems

Umenberger, Jack; Wågberg, Johan; Manchester, Ian R.; Schön, Thomas B.

Ingår i Automatica, s. 280-292, 2018

• DOI för Maximum likelihood identification of stable linear dynamical systems

Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes

Solin, Arno; Kok, Manon; Wahlström, Niklas; Schön, Thomas B. et al.

Ingår i IEEE Transactions on robotics, s. 1112-1127, 2018

• DOI för Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes

System identification through online sparse Gaussian process regression with input noise

Bijl, Hildo; Schön, Thomas B.; van Wingerden, Jan-Willem; Verhaegen, Michel

Ingår i IFAC Journal of Systems and Control, s. 1-11, 2017

• DOI för System identification through online sparse Gaussian process regression with input noise

Divide-and-Conquer with sequential Monte Carlo

Lindsten, Fredrik; Johansen, A. M.; Naesseth, C. A.; Kirkpatrick, B. et al.

Ingår i Journal of Computational And Graphical Statistics, s. 445-458, 2017

• DOI för Divide-and-Conquer with sequential Monte Carlo

Smoothed State Estimation via Efficient Solution of Linear Equations

Geng, Li-Hui; Ninness, Brett; Wills, Adrian; Schön, Thomas B.

Ingår i IFAC-PapersOnLine, s. 1613-1618, 2017

• DOI för Smoothed State Estimation via Efficient Solution of Linear Equations

A flexible state–space model for learning nonlinear dynamical systems

Svensson, Andreas; Schön, Thomas B.

Ingår i Automatica, s. 189-199, 2017

• DOI för A flexible state–space model for learning nonlinear dynamical systems

On robust input design for nonlinear dynamical models

Valenzuela, Patricio E.; Dahlin, Johan; Rojas, Cristian R.; Schön, Thomas B.

Ingår i Automatica, s. 268-278, 2017

• DOI för On robust input design for nonlinear dynamical models

Rao–Blackwellized particle smoothers for conditionally linear Gaussian models

Lindsten, Fredrik; Bunch, Pete; Särkkä, Simo; Schön, Thomas B. et al.

Ingår i IEEE Journal on Selected Topics in Signal Processing, s. 353-365, 2016

• DOI för Rao–Blackwellized particle smoothers for conditionally linear Gaussian models

Using convolution to estimate the score function for intractable state-transition models

Dai, Liang; Schön, Thomas B.

Ingår i IEEE Signal Processing Letters, s. 498-501, 2016

• DOI för Using convolution to estimate the score function for intractable state-transition models

Mean and variance of the LQG cost function

Bijl, Hildo; van Wingerden, Jan-Willem; Schön, Thomas B.; Verhaegen, Michel

Ingår i Automatica, s. 216-223, 2016

• DOI för Mean and variance of the LQG cost function

Magnetometer calibration using inertial sensors

Kok, Manon; Schön, Thomas B.

Ingår i IEEE Sensors Journal, s. 5679-5689, 2016

• DOI för Magnetometer calibration using inertial sensors

A new structure exploiting derivation of recursive direct weight optimization

Dai, Liang; Schön, Thomas B.

Ingår i IEEE Transactions on Automatic Control, s. 1683-1685, 2015

• DOI för A new structure exploiting derivation of recursive direct weight optimization

Indoor positioning using ultrawideband and inertial measurements

Kok, Manon; Hol, Jeroen D.; Schön, Thomas B.

Ingår i IEEE Transactions on Vehicular Technology, s. 1293-1303, 2015

• DOI för Indoor positioning using ultrawideband and inertial measurements

On the exponential convergence of the Kaczmarz algorithm

Dai, Liang; Schön, Thomas B.

Ingår i IEEE Signal Processing Letters, s. 1571-1574, 2015

• DOI för On the exponential convergence of the Kaczmarz algorithm

Particle Metropolis–Hastings using gradient and Hessian information

Dahlin, Johan; Lindsten, Fredrik; Schön, Thomas B.

Ingår i Statistics and computing, s. 81-92, 2015

• DOI för Particle Metropolis–Hastings using gradient and Hessian information

Particle Gibbs with ancestor sampling

Lindsten, Fredrik; Jordan, Michael I.; Schön, Thomas B.

Ingår i Journal of machine learning research, s. 2145-2184, 2014

Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey

Martin, Tim; Schön, Thomas B.; Allgöwer, Frank

Ingår i Annual Reviews in Control, 2023

• DOI för Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey

Sequential Monte Carlo: A Unified Review

Wills, Adrian G.; Schön, Thomas B.

Ingår i ANNUAL REVIEW OF CONTROL ROBOTICS AND AUTONOMOUS SYSTEMS, s. 159-182, 2023

• DOI för Sequential Monte Carlo: A Unified Review
• Ladda ner fulltext (pdf) av Sequential Monte Carlo: A Unified Review

Machine learning: a first course for engineers and scientists

Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.

Cambridge University Press, 2022

Using Inertial Sensors for Position and Orientation Estimation

Kok, Manon; Hol, Jeroen D.; Schön, Thomas B.

Now Publishers Inc., 2017

• DOI för Using Inertial Sensors for Position and Orientation Estimation

A Model Predictive Control Approach to Motion Planning in Dynamic Environments

Wullt, Bernhard; Mattsson, Per; Schön, Thomas B.; Norrlöf, Mikael

Ingår i 2024 European Control Conference (ECC), s. 3247-3254, 2024

• DOI för A Model Predictive Control Approach to Motion Planning in Dynamic Environments

Online Learning in Motion Modeling for Intra-interventional Image Sequences

Gunnarsson, Niklas; Sjölund, Jens; Kimstrand, Peter; Schön, Thomas B.

Ingår i Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, s. 706-716, 2024

• DOI för Online Learning in Motion Modeling for Intra-interventional Image Sequences
• Ladda ner fulltext (pdf) av Online Learning in Motion Modeling for Intra-interventional Image Sequences

Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models

Luo, Ziwei; Gustafsson, Fredrik K.; Zhao, Zheng; Sjölund, Jens et al.

Ingår i 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2024, s. 6641-6651, 2024

• DOI för Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models

Controlling Vision-Language Models for Multi-Task Image Restoration

Luo, Ziwei; Gustafsson, Fredrik K.; Zhao, Zheng; Sjölund, Jens et al.

2024

• Ladda ner fulltext (pdf) av Controlling Vision-Language Models for Multi-Task Image Restoration

NTIRE 2024 restore any image model (RAIM) in the wild challenge

Luo, Ziwei; Gustafsson, Fredrik K.; Zhao, Zheng; Sjölund, Jens et al.

Ingår i 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2024, s. 6632-6640, 2024

• DOI för NTIRE 2024 restore any image model (RAIM) in the wild challenge

No Double Descent in Principal Component Regression: A High-Dimensional Analysis

Gedon, Daniel; Horta Ribeiro, Antônio; Schön, Thomas B.

Ingår i International Conference on Machine Learning (ICML), 2024

Entropy-regularized diffusion policy with Q-ensembles for offline reinforcement learning

Zhang, Ruoqi; Luo, Ziwei; Sjölund, Jens; Schön, Thomas B. et al.

Ingår i Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 2024

On Feynman–Kac training of partial Bayesian neural networks

Zhao, Zheng; Mair, Sebastian; Schön, Thomas B.; Sjölund, Jens

Ingår i Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), s. 3223-3231, 2024

• Ladda ner fulltext (pdf) av On Feynman–Kac training of partial Bayesian neural networks

Uncertainty Estimation with Recursive Feature Machines

Gedon, Daniel; Abedsoltan, Amirhesam; Schön, Thomas B.; Belkin, Mikhail

2024

Regularization properties of adversarially-trained linear regression

Horta Ribeiro, Antônio; Zachariah, Dave; Bach, Francis; Schön, Thomas B.

Ingår i Advances in Neural Information Processing Systems 36 (NeurIPS 2023), s. 23658-23670, 2023

NTIRE 2023 Image Shadow Removal Challenge Report

Vasluianu, Florin -Alexandru; Luo, Ziwei; Gustafsson, Fredrik K.; Zhao, Zheng et al.

Ingår i 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), s. 1788-1807, 2023

• DOI för NTIRE 2023 Image Shadow Removal Challenge Report

Lens-to-Lens Bokeh Effect Transformation: NTIRE 2023 Challenge Report

Conde, Marcus V.; Luo, Ziwei; Gustafsson, Fredrik K.; Zhao, Zheng et al.

Ingår i 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, s. 1643-1659, 2023

• DOI för Lens-to-Lens Bokeh Effect Transformation: NTIRE 2023 Challenge Report

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

Ancuti, Codruta O.; Luo, Ziwei; Gustafsson, Fredrik K.; Zhao, Zheng et al.

Ingår i 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023

• DOI för NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

Refusion: Enabling Large-Size Realistic Image Restoration With Latent-Space Diffusion Models

Luo, Ziwei; Gustafsson, Fredrik K.; Zhao, Zheng; Sjölund, Jens et al.

Ingår i 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), s. 1680-1691, 2023

• DOI för Refusion: Enabling Large-Size Realistic Image Restoration With Latent-Space Diffusion Models

NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results

Wang, Longguang; Luo, Ziwei; Gustafsson, Fredrik K.; Zhao, Zheng et al.

Ingår i IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), s. 1346-1372, 2023

• DOI för NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results

Unsupervised dynamic modeling of medical image transformations

Gunnarsson, Niklas; Sjölund, Jens; Kimstrand, Peter; Schön, Thomas B.

Ingår i 2022 25th International Conference on Information Fusion (FUSION 2022), s. 1-7, 2022

• DOI för Unsupervised dynamic modeling of medical image transformations

Learning Proposals for Practical Energy-Based Regression

Gustafsson, Fredrik K.; Danelljan, Martin; Schön, Thomas B.

Ingår i International conference on artificial intelligence and statistics, vol 151, s. 4685-4704, 2022

Learning a Deformable Registration Pyramid

Gunnarsson, Niklas; Sjölund, Jens; Schön, Thomas B.

Ingår i Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, s. 80-86, 2021

• DOI för Learning a Deformable Registration Pyramid
• Ladda ner fulltext (pdf) av Learning a Deformable Registration Pyramid

Accurate 3D Object Detection using Energy-Based Models

Gustafsson, Fredrik K.; Danelljan, Martin; Schön, Thomas B.

Ingår i 2021 IEEE/CVF Conference on Computer Vision and Pattern Recogition Workshops (CVPRW 2021), s. 2849-2858, 2021

• DOI för Accurate 3D Object Detection using Energy-Based Models

Learning deep autoregressive models for hierarchical data

Andersson, Carl R.; Wahlström, Niklas; Schön, Thomas B.

Ingår i IFAC PapersOnLine, s. 529-534, 2021

• DOI för Learning deep autoregressive models for hierarchical data
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Deep State Space Models for Nonlinear System Identification

Gedon, Daniel; Wahlström, Niklas; Schön, Thomas B.; Ljung, Lennart

Ingår i IFAC PapersOnLine, s. 481-486, 2021

• DOI för Deep State Space Models for Nonlinear System Identification
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First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG

Gedon, Daniel; Horta Ribeiro, Antônio; Wahlström, Niklas; Schön, Thomas B.

Ingår i 2021 Computing In Cardiology (CINC), 2021

• DOI för First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG

Deep Energy-Based NARX Models

Hendriks, Johannes N.; Gustafsson, Fredrik K.; Ribeiro, Antonio H.; Wills, Adrian G. et al.

Ingår i IFAC PapersOnLine, s. 505-510, 2021

• DOI för Deep Energy-Based NARX Models
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How convolutional neural networks deal with aliasing

Horta Ribeiro, Antônio; Schön, Thomas B.

Ingår i 2021 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP 2021), s. 2755-2759, 2021

• DOI för How convolutional neural networks deal with aliasing

Willems' fundamental lemma based on second-order moments

Ferizbegovic, Mina; Hjalmarsson, Hakan; Mattsson, Per; Schön, Thomas B.

Ingår i 2021 60th IEEE Conference On Decision And Control (CDC), s. 396-401, 2021

• DOI för Willems' fundamental lemma based on second-order moments

Variational State and Parameter Estimation

Courts, Jarrad; Hendriks, Johannes; Wills, Adrian; Schön, Thomas B. et al.

Ingår i IFAC PapersOnLine, s. 732-737, 2021

• DOI för Variational State and Parameter Estimation
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Bayes Control of Hammerstein Systems

Ferizbegovic, Mina; Mattsson, Per; Schön, Thomas B.; Hjalmarsson, Håkan

Ingår i IFAC PapersOnLine, s. 755-760, 2021

• DOI för Bayes Control of Hammerstein Systems
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Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics

Ribeiro, Antonio H.; Hendriks, Johannes N.; Wills, Adrian G.; Schön, Thomas B.

Ingår i IFAC PapersOnLine, s. 97-102, 2021

• DOI för Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
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Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble

Ribeiro, Antônio H.; Gedon, Daniel; Martins Teixeira, Daniel; Horta Ribeiro, Manoel et al.

Ingår i 2020 Computing in Cardiology, 2020

• DOI för Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble

Energy-Based Models for Deep Probabilistic Regression

Gustafsson, Fredrik K.; Danelljan, Martin; Bhat, Goutam; Schön, Thomas B.

2020

How to Train Your Energy-Based Model for Regression

Gustafsson, Fredrik K.; Danelljan, Martin; Timofte, Radu; Schön, Thomas B.

2020

Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision

Gustafsson, Fredrik K.; Danelljan, Martin; Schön, Thomas B.

Ingår i 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2020), s. 1289-1298, 2020

• DOI för Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision

Optimistic robust linear quadratic dual control

Umenberger, Jack; Schön, Thomas B.

Ingår i Proceedings of Machine Learning Research, VOL 120, s. 550-560, 2020

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Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness

Ribeiro, Antonio H.; Tiels, Koen; Aguirre, Luis A.; Schön, Thomas B.

Ingår i Proceedings of the 23^rd International Conference on Artificial Intelligence and Statistics (AISTATS), s. 2370-2380, 2020

Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling

Kudlicka, Jan; Murray, Lawrence M.; Ronquist, Fredrik; Schön, Thomas B.

Ingår i The 35th Uncertainty in Artificial Intelligence Conference (UAI), s. 679-689, 2020

Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood

Kudlicka, Jan; Murray, Lawrence M.; Schön, Thomas B.; Lindsten, Fredrik

Ingår i ICASSP 2020, s. 5860-5864, 2020

• DOI för Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood

Deep Learning and System Identification

Ljung, Lennart; Andersson, Carl; Tiels, Koen; Schön, Thomas B.

Ingår i IFAC Papersonline, s. 1175-1181, 2020

• DOI för Deep Learning and System Identification
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A fast quasi-Newton-type method for large-scale stochastic optimisation

Wills, Adrian; Schön, Thomas B.; Jidling, Carl

Ingår i IFAC PapersOnline, s. 1249-1254, 2020

• DOI för A fast quasi-Newton-type method for large-scale stochastic optimisation
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Bayesian identification of state-space models via adaptive thermostats

Umenberger, Jack; Schön, Thomas B.; Lindsten, Fredrik

Ingår i 2019 IEEE 58th conference on decision and control (CDC), s. 7382-7388, 2019

Evaluating model calibration in classification

Vaicenavicius, Juozas; Widmann, David; Andersson, Carl; Lindsten, Fredrik et al.

Ingår i 22nd International Conference on Artificial Intelligence and Statistics, s. 3459-3467, 2019

Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding

Osama, Muhammad; Zachariah, Dave; Schön, Thomas B.

Ingår i Proceedings of the 36th International Conference on Machine Learning, s. 4942-4950, 2019

Deep convolutional networks in system identification

Andersson, Carl; Horta Ribeiro, Antônio; Tiels, Koen; Wahlström, Niklas et al.

Ingår i Proc. 58th IEEE Conference on Decision and Control, s. 3670-3676, 2019

• DOI för Deep convolutional networks in system identification

Robust exploration in linear quadratic reinforcement learning

Umenberger, Jack; Ferizbegovic, Mina; Schön, Thomas B.; Hjalmarsson, Hakan

Ingår i Advances in Neural Information Processing Systems 32 (NIPS 2019), 2019

Conditionally Independent Multiresolution Gaussian Processes

Taghia, Jalil; Schön, Thomas B.

Ingår i 22nd International Conference On Artificial Intelligence And Statistics, Vol 89, 2019

Automatic diagnosis of short-duration 12-lead ECG using a deep convolutional network

Ribeiro, Antonio; Ribeiro, Manoel; Paixao, Gabriela; Oliveira, Derick et al.

Ingår i ML4H: Machine Learning for Health Workshop at NeurIPS, Montréal, Canada, December 2018., 2018

Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations

Svensson, Andreas; Lindsten, Fredrik; Schön, Thomas B.

s. 652-657, 2018

• DOI för Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations

Learning localized spatio-temporal models from streaming data

Osama, Muhammad; Zachariah, Dave; Schön, Thomas

Ingår i Proceedings of the 35th International Conference on Machine Learning, s. 3927-3935, 2018

Regularized parametric system identification: a decision-theoretic formulation

Wågberg, Johan; Zachariah, Dave; Schön, Thomas B.

Ingår i 2018 Annual American Control Conference (ACC), s. 1895-1900, 2018

• DOI för Regularized parametric system identification: a decision-theoretic formulation

Data-driven impulse response regularization via deep learning

Andersson, Carl; Wahlström, Niklas; Schön, Thomas B.

s. 1-6, 2018

• DOI för Data-driven impulse response regularization via deep learning

Delayed sampling and automatic Rao-Blackwellization of probabilistic programs

Murray, Lawrence; Lundén, Daniel; Kudlicka, Jan; Broman, David et al.

Ingår i Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Lanzarote, Spain, April, 2018, 2018

Auxiliary-Particle-Filter-based Two-Filter Smoothing for Wiener State-Space Models

Hostettler, Roland; Schön, Thomas B.

Ingår i Proceedings of the 21st International Conference on Information Fusion, Cambridge, UK, July, 2018., s. 1904-1911, 2018

• DOI för Auxiliary-Particle-Filter-based Two-Filter Smoothing for Wiener State-Space Models

Learning convex bounds for linear quadratic control policy synthesis

Umenberger, Jack; Schön, Thomas B.

Ingår i Neural Information Processing Systems 2018, 2018

How consistent is my model with the data?: Information-theoretic model check

Svensson, Andreas; Zachariah, Dave; Schön, Thomas B.

s. 407-412, 2018

• DOI för How consistent is my model with the data?: Information-theoretic model check

Probabilistic programming allows for automated inference in factor graph models

Scandella, Matteo; Murray, Lawrence; Schön, Thomas B.

2018

Linearly constrained Gaussian processes

Jidling, Carl; Wahlström, Niklas; Wills, Adrian; Schön, Thomas B.

Ingår i Proc. 31st Conference on Neural Information Processing Systems, s. 1215-1224, 2017

Prediction Performance After Learning in Gaussian Process Regression

Wågberg, Johan; Zachariah, Dave; Schön, Thomas B.; Stoica, Peter

Ingår i Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, s. 1264-1272, 2017

• Ladda ner fulltext (pdf) av Prediction Performance After Learning in Gaussian Process Regression

On the construction of probabilistic Newton-type algorithms

Wills, Adrian G.; Schön, Thomas B.

Ingår i Proc. 56th Conference on Decision and Control, s. 6499-6504, 2017

• DOI för On the construction of probabilistic Newton-type algorithms

Particle-based Gaussian process optimization for input design in nonlinear dynamical models

Valenzuela, Patricio L.; Dahlin, Johan; Rojas, Cristian R.; Schön, Thomas B.

Ingår i 2016 IEEE 55th Conference On Decision And Control (CDC), s. 2085-2090, 2016

• DOI för Particle-based Gaussian process optimization for input design in nonlinear dynamical models

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

Svensson, Andreas; Solin, Arno; Särkkä, Simo; Schön, Thomas B.

Ingår i Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, s. 213-221, 2016

Accelerometer calibration using sensor fusion with a gyroscope

Olsson, Fredrik; Kok, Manon; Halvorsen, Kjartan; Schön, Thomas B.

Ingår i Proc. 19th Statistical Signal Processing Workshop, s. 660-664, 2016

• DOI för Accelerometer calibration using sensor fusion with a gyroscope
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Prediction performance after learning in Gaussian process regression

Wågberg, Johan; Zachariah, Dave; Schön, Thomas B.; Stoica, Peter

Ingår i 25th European Research Network System Identification Workshop, 2016

A scalable and distributed solution to the inertial motion capture problem

Kok, Manon; Khoshfetrat Pakazad, Sina; Schön, Thomas B.; Hansson, Anders et al.

Ingår i Proc. 19th International Conference on Information Fusion, s. 1348-1355, 2016

Marginalizing Gaussian process hyperparameters using sequential Monte Carlo

Svensson, Andreas; Dahlin, Johan; Schön, Thomas B.

Ingår i Proc. 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, s. 477-480, 2015

• DOI för Marginalizing Gaussian process hyperparameters using sequential Monte Carlo
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Particle filtering based identification for autonomous nonlinear ODE models

Nordh, Jerker; Wigren, Torbjörn; Schön, Thomas B.; Bernhardsson, Bo

Ingår i Proc. 17th IFAC Symposium on System Identification, s. 415-420, 2015

• DOI för Particle filtering based identification for autonomous nonlinear ODE models

Nonlinear state space model identification using a regularized basis function expansion

Svensson, Andreas; Schön, Thomas B.; Solin, Arno; Särkkä, Simo

Ingår i Proc. 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, s. 481-484, 2015

• DOI för Nonlinear state space model identification using a regularized basis function expansion

Nonlinear state space smoothing using the conditional particle filter

Svensson, Andreas; Schön, Thomas B.; Kok, Manon

Ingår i Proc. 17th IFAC Symposium on System Identification, s. 975-980, 2015

• DOI för Nonlinear state space smoothing using the conditional particle filter

Bayesian nonparametric identification of piecewise affine ARX systems

Wågberg, Johan; Lindsten, Fredrik; Schön, Thomas B.

s. 709-714, 2015

• DOI för Bayesian nonparametric identification of piecewise affine ARX systems

On Identification via EM with Latent Disturbances and Lagrangian Relaxation

Umenberger, Jack; Wågberg, Johan; Manchester, Ian R.; Schön, Thomas B.

s. 69-74, 2015

• DOI för On Identification via EM with Latent Disturbances and Lagrangian Relaxation

Identification of jump Markov linear models using particle filters

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

Ingår i Proc. 53rd Conference on Decision and Control, s. 6504-6509, 2014

• DOI för Identification of jump Markov linear models using particle filters

Backward sequential Monte Carlo for marginal smoothing

Kronander, Joel; Schön, Thomas B.; Dahlin, Johan

Ingår i Proc. 18th Workshop on Statistical Signal Processing, s. 368-371, 2014

• DOI för Backward sequential Monte Carlo for marginal smoothing

Robust auxiliary particle filters using multiple importance sampling

Kronander, Joel; Schön, Thomas B.

Ingår i Proc. 18th Workshop on Statistical Signal Processing, s. 268-271, 2014

• DOI för Robust auxiliary particle filters using multiple importance sampling

Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models

Arteaga, Gabriel Y.; Schön, Thomas B.; Pielawski, Nicolas

2024

• Ladda ner fulltext (pdf) av Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models

Diffusion-Based 3D Motion Estimation from Sparse 2D Observations

Gunnarsson, Niklas; Schön, Thomas B.; Sjölund, Jens; Kimstrand, Peter

2023

Deep kernel learning for integral measurements

Jidling, Carl; Hendriks, Johannes; Schön, Thomas B.; Wills, Adrian

2019

Derivation and validation of STOPSMOKE: An instrument built from Swedish population data for predicting smoking abstinence post myocardial infarction

Wallert, John; Pingel, Ronnie; Schön, Thomas B.; Olsson, Erik et al.

Latent linear dynamics in spatiotemporal medical data

Gunnarsson, Niklas; Sjölund, Jens; Kimstrand, Peter; Schön, Thomas B.

Registration by tracking for sequential 2D MRI

Gunnarsson, Niklas; Sjölund, Jens; Schön, Thomas B.

Rättsliga hinder mot samverkan?: Juridik och ledningsfrågor vid doktorandsamverkan

Strand, Magnus; Wedlin, Linda; Schön, Thomas B.

2025

Comparing two recent particle filter implementations of Bayesian system identification

Svensson, Andreas; Schön, Thomas B.

2016

• Ladda ner fulltext (pdf) av Comparing two recent particle filter implementations of Bayesian system identification

Underlätta samarbeten mellan företag och universitet

Strand, Magnus; Wedlin, Linda; Schön, Thomas B.

2025