882 citations · 2.6k across the 89 of their papers we have counts for
114 papers
On the role of the tokenizer in ECG transformer models
Jiawei Li, Fabio Bonassi, Johan Sundström +2
Tokenization determines both the physiological content presented to an ECG Transformer and the sequence over which attention operates. We compare eight tokenization strategies acro…
A Rigorous, Tractable Measure of Model Complexity
Oskar Allerbo, Thomas B. Schön
An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, most existing complexity measures either…
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,…
Simultaneous State Estimation and Online Model Learning in a Soft Robotic System
Jan-Hendrik Ewering, Max Bartholdt, Simon F. G. Ehlers +3
Operating complex real-world systems, such as soft robots, can benefit from precise predictive control schemes that require accurate state and model knowledge. This knowledge is ty…
Robust Convex Model Predictive Control with collision avoidance guarantees for robot manipulators
Bernhard Wullt, Johannes Köhler, Per Mattsson +2
Industrial manipulators typically operate in cluttered environments, where safe motion planning is critical. However, model uncertainties further complicate this task, which leads…
On the equivalence of direct and indirect data-driven predictive control approaches
Per Mattsson, Fabio Bonassi, Valentina Breschi +1
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-outp…
Safe reinforcement learning in uncertain contexts
Dominik Baumann, Thomas B. Schön
When deploying machine learning algorithms in the real world, guaranteeing safety is an essential asset. Existing safe learning approaches typically consider continuous variables,…
Structured state-space models are deep Wiener models
Fabio Bonassi, Carl Andersson, Per Mattsson +1
The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs). These models have become recently popular in the mac…
Variational Elliptical Processes
Maria Bånkestad, Jens Sjölund, Jalil Taghia +1
We present elliptical processes, a family of non-parametric probabilistic models that subsume Gaussian processes and Student's t processes. This generalization includes a range of…
Regularization properties of adversarially-trained linear regression
Antônio H. Ribeiro, Dave Zachariah, Francis Bach +1
State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to de…
Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey
Tim Martin, Thomas B. Schön, Frank Allgöwer
This survey presents recent research on determining control-theoretic properties and designing controllers with rigorous guarantees using semidefinite programming and for nonlinear…
On the trade-off between event-based and periodic state estimation under bandwidth constraints
Dominik Baumann, Thomas B. Schön
Event-based methods carefully select when to transmit information to enable high-performance control and estimation over resource-constrained communication networks. However, they…
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
Many important computer vision applications are naturally formulated as regression problems. Within medical imaging, accurate regression models have the potential to automate vario…
Image Restoration with Mean-Reverting Stochastic Differential Equations
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a…
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods
Philipp Von Bachmann, Daniel Gedon, Fredrik K. Gustafsson +5
Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outc…
Gaussian inference for data-driven state-feedback design of nonlinear systems
Tim Martin, Thomas B. Schön, Frank Allgöwer
Data-driven control of nonlinear systems with rigorous guarantees is a challenging problem as it usually calls for nonconvex optimization and requires often knowledge of the true b…
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
Anna Wigren, Johan Wågberg, Fredrik Lindsten +2
Identification of nonlinear systems is a challenging problem. Physical knowledge of the system can be used in the identification process to significantly improve the predictive per…
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
Zheng Zhao, Simo Särkkä, Jens Sjölund +1
We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessib…
Incorporating Sum Constraints into Multitask Gaussian Processes
Philipp Pilar, Carl Jidling, Thomas B. Schön +1
Machine learning models can be improved by adapting them to respect existing background knowledge. In this paper we consider multitask Gaussian processes, with background knowledge…
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
Conor Rosato, Vincent Beraud, Paul Horridge +2
It has been widely documented that the sampling and resampling steps in particle filters cannot be differentiated. The {\itshape reparameterisation trick} was introduced to allow t…
Learning Proposals for Practical Energy-Based Regression
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression. However, energy…
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…
Data to Controller for Nonlinear Systems: An Approximate Solution
Johannes N. Hendriks, James R. Z. Holdsworth, Adrian G. Wills +2
This paper considers the problem of determining an optimal control action based on observed data. We formulate the problem assuming that the system can be modelled by a nonlinear s…
Unsupervised dynamic modeling of medical image transformation
Niklas Gunnarsson, Peter Kimstrand, Jens Sjölund +1
Spatiotemporal imaging has applications in e.g. cardiac diagnostics, surgical guidance, and radiotherapy monitoring, In this paper, we explain the temporal motion by identifying th…