b'Simo S\xc3\xa4rkk\xc3\xa4'

research

∙ 05/05/2023

Fast Dynamic Programming in Trees in the MPC Model

We present a deterministic algorithm for solving a wide range of dynamic...

0 Chetan Gupta, et al. ∙

research

∙ 03/30/2023

Gaussian-Based Parametric Bijections For Automatic Projection Filters

The automatic projection filter is a recently developed numerical method...

0 Muhammad F. Emzir, et al. ∙

research

∙ 03/01/2023

Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems

We introduce two new classes of exact Markov chain Monte Carlo (MCMC) sa...

0 Adrien Corenflos, et al. ∙

research

∙ 12/22/2022

Temporal Parallelisation of the HJB Equation and Continuous-Time Linear Quadratic Control

This paper presents a mathematical formulation to perform temporal paral...

0 Simo Särkkä, et al. ∙

research

∙ 08/17/2022

Metal artifact correction in cone beam computed tomography using synthetic X-ray data

Metal artifact correction is a challenging problem in cone beam computed...

10 Harshit Agrawal, et al. ∙

research

∙ 08/15/2022

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Robust and accurate localization is a basic requirement for mobile auton...

0 Hao Dong, et al. ∙

research

∙ 06/29/2022

Parallel square-root statistical linear regression for inference in nonlinear state space models

In this article, we introduce parallel-in-time methods for state and par...

0 Fatemeh Yaghoobi, et al. ∙

research

∙ 05/12/2022

Probabilistic Estimation of Chirp Instantaneous Frequency Using Gaussian Processes

We present a probabilistic approach for estimating chirp signal and its ...

0 Zheng Zhao, et al. ∙

research

∙ 02/04/2022

De-Sequentialized Monte Carlo: a parallel-in-time particle smoother

Particle smoothers are SMC (Sequential Monte Carlo) algorithms designed ...

1 Adrien Corenflos, et al. ∙

research

∙ 01/24/2022

The Coupled Rejection Sampler

We propose a coupled rejection-sampling method for sampling from couplin...

0 Adrien Corenflos, et al. ∙

research

∙ 01/22/2022

Uncertainty-aware deep learning methods for robust diabetic retinopathy classification

Automatic classification of diabetic retinopathy from retinal images has...

12 Joel Jaskari, et al. ∙

research

∙ 12/16/2021

Multidimensional Projection Filters via Automatic Differentiation and Sparse-Grid Integration

The projection filter is a method for approximating the dynamics of cond...

0 Muhammad Fuady Emzir, et al. ∙

research

∙ 12/07/2021

Posterior linearisation smoothing with robust iterations

This paper considers the problem of robust iterative Bayesian smoothing ...

0 Jakob Lindqvist, et al. ∙

research

∙ 11/02/2021

Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees

We formulate natural gradient variational inference (VI), expectation pr...

0 William J. Wilkinson, et al. ∙

research

∙ 09/30/2021

Non-linear Gaussian smoothing with Taylor moment expansion

This letter is concerned with solving continuous-discrete Gaussian smoot...

0 Zheng Zhao, et al. ∙

research

∙ 05/20/2021

Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization

This paper is concerned with regularized extensions of hierarchical non-...

0 Zheng Zhao, et al. ∙

research

∙ 05/15/2021

Kalman filtering with empirical noise models

Most Kalman filter extensions assume Gaussian noise and when the noise i...

0 Matti Raitoharju, et al. ∙

research

∙ 04/25/2021

System identification using Bayesian neural networks with nonparametric noise models

System identification is of special interest in science and engineering....

0 Christos Merkatas, et al. ∙

research

∙ 04/07/2021

Temporal Parallelisation of Dynamic Programming and Linear Quadratic Control

This paper proposes a method for temporal parallelisation of dynamic pro...

0 Simo Särkkä, et al. ∙

research

∙ 02/19/2021

Temporal Gaussian Process Regression in Logarithmic Time

The aim of this article is to present a novel parallelization method for...

0 Adrien Corenflos, et al. ∙

research

∙ 02/10/2021

Temporal Parallelization of Inference in Hidden Markov Models

This paper presents algorithms for parallelization of inference in hidde...

0 Sakira Hassan, et al. ∙

research

∙ 02/01/2021

A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations

We study a class of Gaussian processes for which the posterior mean, for...

0 Toni Karvonen, et al. ∙

research

∙ 01/31/2021

Parallel Iterated Extended and Sigma-point Kalman Smoothers

The problem of Bayesian filtering and smoothing in nonlinear models with...

0 Fatemeh Yaghoobi, et al. ∙

research

∙ 08/11/2020

Deep State-Space Gaussian Processes

This paper is concerned with a state-space approach to deep Gaussian pro...

8 Zheng Zhao, et al. ∙

research

∙ 07/22/2020

Autonomous Tracking and State Estimation with Generalised Group Lasso

We address the problem of autonomous tracking and state estimation for m...

0 Rui Gao, et al. ∙

research

∙ 06/28/2020

Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion

In this article, we study Bayesian inverse problems with multi-layered G...

0 Muhammad Emzir, et al. ∙

research

∙ 05/17/2020

Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes

In this letter, we propose a class of efficient, accurate and general me...

0 Rui Gao, et al. ∙

research

∙ 04/17/2020

Continuous-Discrete Filtering and Smoothing on Submanifolds of Euclidean Space

In this paper the issue of filtering and smoothing in continuous discret...

4 Filip Tronarp, et al. ∙

research

∙ 04/01/2020

Bayesian ODE Solvers: The Maximum A Posteriori Estimate

It has recently been established that the numerical solution of ordinary...

40 Filip Tronarp, et al. ∙

research

∙ 03/08/2020

Enhancing Industrial X-ray Tomography by Data-Centric Statistical Methods

X-ray tomography has applications in various industrial fields such as s...

0 Jarkko Suuronen, et al. ∙

research

∙ 01/29/2020

Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions

Despite the ubiquity of the Gaussian process regression model, few theor...

20 Toni Karvonen, et al. ∙

research

∙ 01/08/2020

Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering and Smoothing

The paper is concerned with non-linear Gaussian filtering and smoothing ...

13 Zheng Zhao, et al. ∙

research

∙ 12/16/2019

On the Convergence of Numerical Integration as a Finite Matrix Approximation to Multiplication Operator

We study the convergence of a family of numerical integration methods wh...

0 Juha Sarmavuori, et al. ∙

research

∙ 12/16/2019

Kernel-based interpolation at approximate Fekete points

We construct approximate Fekete point sets for kernel-based interpolatio...

0 Toni Karvonen, et al. ∙

research

∙ 09/06/2019

Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network

Objective: The aim of this study is to develop an automated classificati...

7 Ali Bahrami Rad, et al. ∙

research

∙ 07/13/2019

The Use of Gaussian Processes in System Identification

Gaussian processes are used in machine learning to learn input-output ma...

3 Simo Särkkä, et al. ∙

research

∙ 05/30/2019

Temporal Parallelization of Bayesian Filters and Smoothers

This paper presents algorithms for the temporal parallelization of Bayes...

0 Simo Särkkä, et al. ∙

research

∙ 03/20/2019

Iterated Extended Kalman Smoother-based Variable Splitting for L_1-Regularized State Estimation

In this paper, we propose a new framework for solving state estimation p...

0 Rui Gao, et al. ∙

research

∙ 03/01/2019

1D Convolutional Neural Network Models for Sleep Arousal Detection

Sleep arousals transition the depth of sleep to a more superficial stage...

16 Morteza Zabihi, et al. ∙

research

∙ 12/20/2018

On the positivity and magnitudes of Bayesian quadrature weights

This article reviews and studies the properties of Bayesian quadrature w...

0 Toni Karvonen, et al. ∙

research

∙ 12/12/2018

Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection

In this article, we propose a novel ECG classification framework for atr...

14 Zheng Zhao, et al. ∙

research

∙ 11/28/2018

Improved Calibration of Numerical Integration Error in Sigma-Point Filters

The sigma-point filters, such as the UKF, which exploit numerical quadra...

10 Jakub Prüher, et al. ∙

research

∙ 11/23/2018

LSD_2 - Joint Denoising and Deblurring of Short and Long Exposure Images with Convolutional Neural Networks

This paper addresses the challenging problem of acquiring high-quality p...

4 Janne Mustaniemi, et al. ∙

research

∙ 10/08/2018

Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective

We formulate probabilistic numerical approximations to solutions of ordi...

10 Filip Tronarp, et al. ∙

research

∙ 10/01/2018

Inertial-aided Motion Deblurring with Deep Networks

We propose an inertial-aided deblurring method that incorporates gyrosco...

4 Janne Mustaniemi, et al. ∙

research

∙ 09/26/2018

Symmetry Exploits for Bayesian Cubature Methods

Bayesian cubature provides a flexible framework for numerical integratio...

0 Toni Karvonen, et al. ∙

research

∙ 09/15/2018

On stability of a class of filters for non-linear stochastic systems

This article considers stability properties of a broad class of commonly...

0 Toni Karvonen, et al. ∙

research

∙ 09/13/2018

Gaussian process classification using posterior linearisation

This paper proposes a new algorithm for Gaussian process classification ...

6 Ángel F. García-Fernández, et al. ∙

research

∙ 09/11/2018

Probabilistic approach to limited-data computed tomography reconstruction

We consider the problem of reconstructing the internal structure of an o...

2 Zenith Purisha, et al. ∙

research

∙ 05/29/2018

Iterative Statistical Linear Regression for Gaussian Smoothing in Continuous-Time Non-linear Stochastic Dynamic Systems

This paper considers approximate smoothing for discretely observed non-l...

0 Filip Tronarp, et al. ∙

Simo Särkkä

Featured Co-authors