68 citations · 104 across the 13 of their papers we have counts for
13 papers
Stochastic filtering with moment representation
Zheng Zhao, Juha Sarmavuori
Stochastic filtering refers to estimating the probability distribution of the latent stochastic process conditioned on the observed measurements in time. In this paper, we introduc…
Gaussian-Based Parametric Bijections For Automatic Projection Filters
Muhammad F. Emzir, Zheng Zhao, Lahouari Cheded +1
The automatic projection filter is a recently developed numerical method for projection filtering that leverages sparse-grid integration and automatic differentiation. However, its…
Image Restoration with Mean-Reverting Stochastic Differential Equations
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a…
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
Zheng Zhao, Simo Särkkä, Jens Sjölund +1
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 accessib…
Multidimensional Projection Filters via Automatic Differentiation and Sparse-Grid Integration
Muhammad Fuady Emzir, Zheng Zhao, Simo Särkkä
The projection filter is a technique for approximating the solutions of optimal filtering problems. In projection filters, the Kushner--Stratonovich stochastic partial differential…
State-space deep Gaussian processes with applications
Zheng Zhao
This thesis is mainly concerned with state-space approaches for solving deep (temporal) Gaussian process (DGP) regression problems. More specifically, we represent DGPs as hierarch…
Non-linear Gaussian smoothing with Taylor moment expansion
Zheng Zhao, Simo Särkkä
This letter is concerned with solving continuous-discrete Gaussian smoothing problems by using the Taylor moment expansion (TME) scheme. In the proposed smoothing method, we apply…
Hierarchical Non-Stationary Temporal Gaussian Processes With -Regularization
Zheng Zhao, Rui Gao, Simo Särkkä
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 G…
Temporal Gaussian Process Regression in Logarithmic Time
Adrien Corenflos, Zheng Zhao, Simo Särkkä
The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems. The method allows for solving GP regression problems in…
Deep State-Space Gaussian Processes
Zheng Zhao, Muhammad Emzir, Simo Särkkä
This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed Gaussian process (GP) prio…
Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering and Smoothing
Zheng Zhao, Toni Karvonen, Roland Hostettler +1
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 Itô stochastic differ…
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Ali Bahrami Rad, Morteza Zabihi, Zheng Zhao +3
Objective: The aim of this study is to develop an automated classification algorithm for polysomnography (PSG) recordings to detect non-apneic and non-hypopneic arousals. Our parti…
Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection
Zheng Zhao, Simo Särkkä, Ali Bahrami Rad
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…