47 citations · 131 across the 9 of their papers we have counts for
10 papers
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…
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Filip de Roos, Carl Jidling, Adrian Wills +2
Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms. We provide a probabilistic moti…
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…
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…
Neutron Transmission Strain Tomography for Non-Constant Stress-Free Lattice Spacing
J. N. Hendriks, C. Jidling, T. B. Schön +3
Recently, several algorithms for strain tomography from energy-resolved neutron transmission measurements have been proposed. These methods assume that the stress-free lattice spac…
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…
A fast quasi-Newton-type method for large-scale stochastic optimisation
Adrian Wills, Carl Jidling, Thomas Schon
During recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improv…
Probabilistic approach to limited-data computed tomography reconstruction
Zenith Purisha, Carl Jidling, Niklas Wahlström +2
In this work, we consider the inverse problem of reconstructing the internal structure of an object from limited x-ray projections. We use a Gaussian process prior to model the tar…
Probabilistic modelling and reconstruction of strain
Carl Jidling, Johannes Hendriks, Niklas Wahlström +4
This paper deals with modelling and reconstruction of strain fields, relying upon data generated from neutron Bragg-edge measurements. We propose a probabilistic approach in which…
Linearly constrained Gaussian processes
Carl Jidling, Niklas Wahlström, Adrian Wills +1
We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints. By modelling the target function as a transformation…