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Adjoint-aided inference of Gaussian process driven differential equations
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Adjoint-aided inference of Gaussian process driven differential equations

Paterne Gahungu
Christopher W. Lanyon
Mauricio A. Alvarez
Engineer Bainomugisha
M. Smith
Richard D. Wilkinson
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Papers citing "Adjoint-aided inference of Gaussian process driven differential equations"

12 / 12 papers shown
Title
Bringing PDEs to JAX with forward and reverse modes automatic differentiation
Bringing PDEs to JAX with forward and reverse modes automatic differentiation
I. Yashchuk
59
6
0
31 Aug 2023
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions
P. Greengard
M. O’Neil
53
11
0
12 Aug 2021
Stochastic Variational Bayesian Inference for a Nonlinear Forward Model
Stochastic Variational Bayesian Inference for a Nonlinear Forward Model
M. Chappell
A. Groves
M. Woolrich
26
355
0
03 Jul 2020
Matérn Gaussian processes on Riemannian manifolds
Matérn Gaussian processes on Riemannian manifolds
Viacheslav Borovitskiy
Alexander Terenin
P. Mostowsky
M. Deisenroth
118
123
0
17 Jun 2020
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity
Sam Coveney
C. Corrado
C. Roney
D. O'Hare
Steven E. Williams
M. OÑeill
Steven Niederer
R. Clayton
J. Oakley
Richard D. Wilkinson
48
41
0
22 Apr 2020
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Geoffrey Roeder
Paul K. Grant
Andrew Phillips
Neil Dalchau
Edward Meeds
103
24
0
28 May 2019
A Review of automatic differentiation and its efficient implementation
A Review of automatic differentiation and its efficient implementation
C. Margossian
75
320
0
12 Nov 2018
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes
Cristian Guarnizo Lemus
Mauricio A. Alvarez
76
15
0
18 May 2018
Linearly constrained Gaussian processes
Linearly constrained Gaussian processes
Carl Jidling
Niklas Wahlström
A. Wills
Thomas B. Schon
65
98
0
02 Mar 2017
Hilbert Space Methods for Reduced-Rank Gaussian Process Regression
Hilbert Space Methods for Reduced-Rank Gaussian Process Regression
Arno Solin
Simo Särkkä
224
218
0
21 Jan 2014
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
313
3,282
0
09 Jun 2012
Linear Latent Force Models using Gaussian Processes
Linear Latent Force Models using Gaussian Processes
Mauricio A. Alvarez
D. Luengo
Neil D. Lawrence
107
124
0
13 Jul 2011

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