196 citations · 257 across the 41 of their papers we have counts for
9 papers · 1 filter
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…
Quantum-assisted Gaussian process regression using random Fourier features
Cristian A. Galvis-Florez, Ahmad Farooq, Simo Särkkä
Probabilistic machine learning models are distinguished by their ability to integrate prior knowledge of noise statistics, smoothness parameters, and training data uncertainty. A c…
Provable Quantum Algorithm Advantage for Gaussian Process Quadrature
Cristian A. Galvis-Florez, Ahmad Farooq, Simo Särkkä
The aim of this paper is to develop novel quantum algorithms for Gaussian process quadrature methods. Gaussian process quadratures are numerical integration methods where Gaussian…
Parallel state estimation for systems with integrated measurements
Fatemeh Yaghoobi, Simo Särkkä
This paper presents parallel-in-time state estimation methods for systems with Slow-Rate inTegrated Measurements (SRTM). Integrated measurements are common in various applications,…
Quantum-Assisted Hilbert-Space Gaussian Process Regression
Ahmad Farooq, Cristian A. Galvis-Florez, Simo Särkkä
Gaussian processes are probabilistic models that are commonly used as functional priors in machine learning. Due to their probabilistic nature, they can be used to capture the prio…
Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems
Adrien Corenflos, Simo Särkkä
Sampling from the full posterior distribution of high-dimensional non-linear, non-Gaussian latent dynamical models presents significant computational challenges. While Particle Gib…