Search Results for author:
Found 48 papers, 20 papers with code
Utilizing U-Net Architectures with Auxiliary Information for Scatter Correction in CBCT Across Different Field-of-View Settings
Our findings suggest that this novel approach outperforms the baseline U-Net, offering a significant step towards practical application in real clinical settings where CBCT systems are employed to scan a wide range of FOVs.
Nesting Particle Filters for Experimental Design in Dynamical Systems
In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization.
Quantum-Assisted Hilbert-Space Gaussian Process Regression
Gaussian processes are probabilistic models that are commonly used as functional priors in machine learning.
Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing
Stochastic optimal control of dynamical systems is a crucial challenge in sequential decision-making.
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics
By differentiating treatment components, incorporating their dosages, and sharing statistical information across patients via a hierarchical multi-output Gaussian process, our method improves prediction accuracy over existing approaches, and allows us to interpret the different effects of carbohydrates and fat on the overall glucose response.
Parallel-in-Time Probabilistic Numerical ODE Solvers
Probabilistic numerical solvers for ordinary differential equations (ODEs) treat the numerical simulation of dynamical systems as problems of Bayesian state estimation.
A Recursive Newton Method for Smoothing in Nonlinear State Space Models
In this paper, we use the optimization formulation of nonlinear Kalman filtering and smoothing problems to develop second-order variants of iterated Kalman smoother (IKS) methods.
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) samplers for inference in latent dynamical models.
Deep learning based projection domain metal segmentation for metal artifact reduction in cone beam computed tomography
We compare our model's performance on real clinical scans with conventional region growing threshold-based MAR, moving metal artifact reduction method, and a recent deep learning method.
Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments
We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation.
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessible.
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
We propose dSMC (de-Sequentialized Monte Carlo), a new particle smoother that is able to process $T$ observations in $\mathcal{O}(\log T)$ time on parallel architecture.
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results.
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterior linearisation (PL) as extensions of Newton's method for optimising the parameters of a Bayesian posterior distribution.
Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$-Regularization
This paper is concerned with regularized extensions of hierarchical non-stationary temporal Gaussian processes (NSGPs) in which the parameters (e. g., length-scale) are modeled as GPs.
System identification using Bayesian neural networks with nonparametric noise models
System identification is of special interest in science and engineering.
Temporal Gaussian Process Regression in Logarithmic Time
The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems.
Temporal Parallelization of Inference in Hidden Markov Models
This paper presents algorithms for parallelization of inference in hidden Markov models (HMMs).
A probabilistic Taylor expansion with Gaussian processes
We study a class of Gaussian processes for which the posterior mean, for a particular choice of data, replicates a truncated Taylor expansion of any order.
Deep State-Space Gaussian Processes
This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression.
Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion
In this article, we study Bayesian inverse problems with multi-layered Gaussian priors.
Statistics Theory Statistics Theory
Continuous-Discrete Filtering and Smoothing on Submanifolds of Euclidean Space
For approximate filtering and smoothing the projection approach is taken, where it turns out that the prediction and smoothing equations are the same as in the case when the state variable evolves in Euclidean space.
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
We show that the maximum likelihood estimation of the scale parameter alone provides significant adaptation against misspecification of the Gaussian process model in the sense that the model can become "slowly" overconfident at worst, regardless of the difference between the smoothness of the data-generating function and that expected by the model.
Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering and Smoothing
The paper is concerned with non-linear Gaussian filtering and smoothing in continuous-discrete state-space models, where the dynamic model is formulated as an It\^{o} stochastic differential equation (SDE), and the measurements are obtained at discrete time instants.
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Results: The proposed algorithm is validated on the 2018 PhysioNet challenge dataset.
The Use of Gaussian Processes in System Identification
Gaussian process state-space models (GPSS) can be used to learn the dynamic and measurement models for a state-space representation of the input-output data.
1D Convolutional Neural Network Models for Sleep Arousal Detection
Sleep arousals transition the depth of sleep to a more superficial stage.
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 atrial fibrillation (AF) detection using spectro-temporal representation (i. e., time varying spectrum) and deep convolutional networks.
Improved Calibration of Numerical Integration Error in Sigma-Point Filters
The sigma-point filters, such as the UKF, which exploit numerical quadrature to obtain an additional order of accuracy in the moment transformation step, are popular alternatives to the ubiquitous EKF.
LSD$_2$ -- Joint Denoising and Deblurring of Short and Long Exposure Images with CNNs
The paper addresses the problem of acquiring high-quality photographs with handheld smartphone cameras in low-light imaging conditions.
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective
We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions.
Gyroscope-Aided Motion Deblurring with Deep Networks
We propose a deblurring method that incorporates gyroscope measurements into a convolutional neural network (CNN).
Symmetry Exploits for Bayesian Cubature Methods
Bayesian cubature provides a flexible framework for numerical integration, in which a priori knowledge on the integrand can be encoded and exploited.
Methodology Numerical Analysis Computation
Gaussian process classification using posterior linearisation
This paper proposes a new algorithm for Gaussian process classification based on posterior linearisation (PL).
Probabilistic approach to limited-data computed tomography reconstruction
The approach also allows for reformulation of come classical regularization methods as Laplacian and Tikhonov regularization as Gaussian process regression, and hence provides an efficient algorithm and principled means for their parameter tuning.
Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements
It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors.
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
This article is concerned with learning and stochastic control in physical systems which contain unknown input signals.
Student-t Process Quadratures for Filtering of Non-Linear Systems with Heavy-Tailed Noise
Advantage of the Student- t process quadrature over the traditional Gaussian process quadrature, is that the integral variance depends also on the function values, allowing for a more robust modelling of the integration error.
Inertial-Based Scale Estimation for Structure from Motion on Mobile Devices
In the process, we also perform a temporal and spatial alignment of the camera and the IMU.
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models.
A probabilistic model for the numerical solution of initial value problems
Like many numerical methods, solvers for initial value problems (IVPs) on ordinary differential equations estimate an analytically intractable quantity, using the results of tractable computations as inputs.
Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
We apply the method to both simulated and empirical data, and demonstrate the efficiency and generality of our Bayesian source reconstruction approach which subsumes various classical approaches in the literature.
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation.
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure.
Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems
We consider approximate maximum likelihood parameter estimation in nonlinear state-space models.
Methodology Dynamical Systems Optimization and Control Computation
On the relation between Gaussian process quadratures and sigma-point methods
This article is concerned with Gaussian process quadratures, which are numerical integration methods based on Gaussian process regression methods, and sigma-point methods, which are used in advanced non-linear Kalman filtering and smoothing algorithms.
Hilbert Space Methods for Reduced-Rank Gaussian Process Regression
On this approximate eigenbasis the eigenvalues of the covariance function can be expressed as simple functions of the spectral density of the Gaussian process, which allows the GP inference to be solved under a computational cost scaling as $\mathcal{O}(nm^2)$ (initial) and $\mathcal{O}(m^3)$ (hyperparameter learning) with $m$ basis functions and $n$ data points.
The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes
The decomposition allows us to take a diffusion process of interest and cast it in a form that is amenable to sampling by MCMC methods.
