882 citations · 2.6k across the 89 of their papers we have counts for
11 papers · 1 filter
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…
Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network
Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão +7
We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology…
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…
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…
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,…
Stochastic quasi-Newton with adaptive step lengths for large-scale problems
Adrian Wills, Thomas Schön
We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems. Our key innovation is an auxilia…
Conditionally Independent Multiresolution Gaussian Processes
Jalil Taghia, Thomas B. Schön
The multiresolution Gaussian process (GP) has gained increasing attention as a viable approach towards improving the quality of approximations in GPs that scale well to large-scale…
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…
Learning Localized Spatio-Temporal Models From Streaming Data
Muhammad Osama, Dave Zachariah, Thomas B. Schön
We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the trainin…
Data-Driven Impulse Response Regularization via Deep Learning
Carl Andersson, Niklas Wahlström, Thomas B. Schön
We consider the problem of impulse response estimation of stable linear single-input single-output systems. It is a well-studied problem where flexible non-parametric models recent…