91 citations · 198 across the 14 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 kernel learning for integral measurements
Carl Jidling, Johannes Hendriks, Thomas B. Schön +1
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions. We present a method that makes this approach feas…
Constructing the Matrix Multilayer Perceptron and its Application to the VAE
Jalil Taghia, Maria Bånkestad, Fredrik Lindsten +1
Like most learning algorithms, the multilayer perceptrons (MLP) is designed to learn a vector of parameters from data. However, in certain scenarios we are interested in learning s…
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations
J. N. Hendriks, C. Jidling, A. Wills +1
This paper deals with the evaluation of double line integrals of the squared exponential covariance function. We propose a new approach in which the double integral is reduced to a…
Automated learning with a probabilistic programming language: Birch
Lawrence M. Murray, Thomas B. Schön
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Tur…
Learning convex bounds for linear quadratic control policy synthesis
Jack Umenberger, Thomas B. Schön
Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a number of fields, from artificial intelligence and robotics,…