196 citations · 699 across the 80 of their papers we have counts for
12 papers · 1 filter
Temporal parallelisation of continuous-time maximum-a-posteriori trajectory estimation
Hassan Razavi, Ángel F. García-Fernández, Simo Särkkä
This paper proposes a parallel-in-time method for computing continuous-time maximum-a-posteriori (MAP) trajectory estimates of the states of partially observed stochastic different…
On The Performance of Prefix-Sum Parallel Kalman Filters and Smoothers on GPUs
Simo Särkkä, Ángel F. García-Fernández
This paper presents an experimental evaluation of parallel-in-time Kalman filters and smoothers using graphics processing units (GPUs). In particular, the paper evaluates different…
Online Bayesian Experimental Design for Partially Observed Dynamical Systems
Sara Pérez-Vieites, Sahel Iqbal, Simo Särkkä +1
Bayesian experimental design (BED) provides a principled framework for optimizing data collection by choosing experiments that are maximally informative about unknown parameters. H…
A parallel-in-time Newton's method-based ODE solver
Casian Iacob, Hassan Razavi, Simo Särkkä
In this article, we introduce a novel parallel-in-time solver for nonlinear ordinary differential equations (ODEs). We state the numerical solution of an ODE as a root-finding prob…
Ultrafast Deep Learning-Based Scatter Estimation in Cone-Beam Computed Tomography
Harshit Agrawal, Ari Hietanen, Simo Särkkä
Purpose: Scatter artifacts drastically degrade the image quality of cone-beam computed tomography (CBCT) scans. Although deep learning-based methods show promise in estimating scat…
Conditional Normalizing Flow Surrogate for Monte Carlo Prediction of Radiative Properties in Nanoparticle-Embedded Layers
Fahime Seyedheydari, Kevin Conley, Simo Särkkä
We present a probabilistic, data-driven surrogate model for predicting the radiative properties of nanoparticle embedded scattering media. The model uses conditional normalizing fl…