9 citations · 18 across the 3 of their papers we have counts for
7 papers
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…
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…