
Deep StateSpace Gaussian Processes
This paper is concerned with a statespace approach to deep Gaussian pro...
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Autonomous Tracking and State Estimation with Generalised Group Lasso
We address the problem of autonomous tracking and state estimation for m...
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NonStationary Multilayered Gaussian Priors for Bayesian Inversion
In this article, we study Bayesian inverse problems with multilayered G...
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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...
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ContinuousDiscrete Filtering and Smoothing on Submanifolds of Euclidean Space
In this paper the issue of filtering and smoothing in continuous discret...
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Bayesian ODE Solvers: The Maximum A Posteriori Estimate
It has recently been established that the numerical solution of ordinary...
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Enhancing Industrial Xray Tomography by DataCentric Statistical Methods
Xray tomography has applications in various industrial fields such as s...
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Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Despite the ubiquity of the Gaussian process regression model, few theor...
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Taylor Moment Expansion for ContinuousDiscrete Gaussian Filtering and Smoothing
The paper is concerned with nonlinear Gaussian filtering and smoothing ...
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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...
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Kernelbased interpolation at approximate Fekete points
We construct approximate Fekete point sets for kernelbased interpolatio...
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Automated Polysomnography Analysis for Detection of NonApneic and NonHypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Objective: The aim of this study is to develop an automated classificati...
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The Use of Gaussian Processes in System Identification
Gaussian processes are used in machine learning to learn inputoutput ma...
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Temporal Parallelization of Bayesian Filters and Smoothers
This paper presents algorithms for the temporal parallelization of Bayes...
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Iterated Extended Kalman Smootherbased Variable Splitting for L_1Regularized State Estimation
In this paper, we propose a new framework for solving state estimation p...
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1D Convolutional Neural Network Models for Sleep Arousal Detection
Sleep arousals transition the depth of sleep to a more superficial stage...
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On the positivity and magnitudes of Bayesian quadrature weights
This article reviews and studies the properties of Bayesian quadrature w...
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Kalmanbased SpectroTemporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection
In this article, we propose a novel ECG classification framework for atr...
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Improved Calibration of Numerical Integration Error in SigmaPoint Filters
The sigmapoint filters, such as the UKF, which exploit numerical quadra...
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LSD_2  Joint Denoising and Deblurring of Short and Long Exposure Images with Convolutional Neural Networks
This paper addresses the challenging problem of acquiring highquality p...
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Probabilistic Solutions To Ordinary Differential Equations As NonLinear Bayesian Filtering: A New Perspective
We formulate probabilistic numerical approximations to solutions of ordi...
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Inertialaided Motion Deblurring with Deep Networks
We propose an inertialaided deblurring method that incorporates gyrosco...
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Symmetry Exploits for Bayesian Cubature Methods
Bayesian cubature provides a flexible framework for numerical integratio...
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On stability of a class of filters for nonlinear stochastic systems
This article considers stability properties of a broad class of commonly...
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Gaussian process classification using posterior linearisation
This paper proposes a new algorithm for Gaussian process classification ...
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Probabilistic approach to limiteddata computed tomography reconstruction
We consider the problem of reconstructing the internal structure of an o...
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Iterative Statistical Linear Regression for Gaussian Smoothing in ContinuousTime Nonlinear Stochastic Dynamic Systems
This paper considers approximate smoothing for discretely observed nonl...
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Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements
Many computer vision and image processing applications rely on local fea...
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A BayesSard Cubature Method
This paper focusses on the formulation of numerical integration as an in...
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Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
This paper is concerned with estimation and stochastic control in physic...
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Fully symmetric kernel quadrature
Kernel quadratures and other kernelbased approximation methods typicall...
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Studentt Process Quadratures for Filtering of NonLinear Systems with HeavyTailed Noise
The aim of this article is to design a moment transformation for Student...
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InertialBased Scale Estimation for Structure from Motion on Mobile Devices
Structure from motion algorithms have an inherent limitation that the re...
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Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
We propose a parallelizable sparse inverse formulation Gaussian process ...
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A probabilistic model for the numerical solution of initial value problems
Like many numerical methods, solvers for initial value problems (IVPs) o...
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Regularizing Solutions to the MEG Inverse Problem Using SpaceTime Separable Covariance Functions
In magnetoencephalography (MEG) the conventional approach to source reco...
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Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoo...
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Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Gaussian processes allow for flexible specification of prior assumptions...
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On the relation between Gaussian process quadratures and sigmapoint methods
This article is concerned with Gaussian process quadratures, which are n...
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Hilbert Space Methods for ReducedRank Gaussian Process Regression
This paper proposes a novel scheme for reducedrank Gaussian process reg...
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Sequential Inference for Latent Force Models
Latent force models (LFMs) are hybrid models combining mechanistic princ...
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Simo Särkkä
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Associate Professor in Sensor informatics and medical technology at Department of Electrical Engineering and Automation (EEA) at Aalto University