91 citations · 198 across the 14 of their papers we have counts for
36 papers
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…
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…
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…