882 citations · 2.6k across the 89 of their papers we have counts for
10 papers · 1 filter
Variational State and Parameter Estimation
Jarrad Courts, Johannes Hendriks, Adrian Wills +2
This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tract…
Deep Energy-Based NARX Models
Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro +2
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distributio…
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
Antônio H. Ribeiro, Johannes N. Hendriks, Adrian G. Wills +1
System identification aims to build models of dynamical systems from data. Traditionally, choosing the model requires the designer to balance between two goals of conflicting natur…
Variational System Identification for Nonlinear State-Space Models
Jarrad Courts, Adrian Wills, Thomas Schön +1
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational infe…
How to Train Your Energy-Based Model for Regression
Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte +1
Energy-based models (EBMs) have become increasingly popular within computer vision in recent years. While they are commonly employed for generative image modeling, recent work has…
Registration by tracking for sequential 2D MRI
Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön
Our anatomy is in constant motion. With modern MR imaging it is possible to record this motion in real-time during an ongoing radiation therapy session. In this paper we present an…
Deep State Space Models for Nonlinear System Identification
Daniel Gedon, Niklas Wahlström, Thomas B. Schön +1
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. Th…
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes
Maria Bånkestad, Jens Sjölund, Jalil Taghia +1
We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process. This generalization includes a…
Gaussian Variational State Estimation for Nonlinear State-Space Models
Jarrad Courts, Adrian Wills, Thomas B. Schön
In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the m…
Linearly Constrained Neural Networks
Johannes Hendriks, Carl Jidling, Adrian Wills +1
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints. To achiev…