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Tensor Monte Carlo: particle methods for the GPU era

Tensor Monte Carlo: particle methods for the GPU era

22 June 2018
Laurence Aitchison
    BDL
    DRL
ArXivPDFHTML

Papers citing "Tensor Monte Carlo: particle methods for the GPU era"

7 / 7 papers shown
Title
Massively Parallel Reweighted Wake-Sleep
Massively Parallel Reweighted Wake-Sleep
Thomas Heap
Gavin Leech
Laurence Aitchison
BDL
18
2
0
18 May 2023
Multiple Importance Sampling ELBO and Deep Ensembles of Variational
  Approximations
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
Oskar Kviman
Harald Melin
Hazal Koptagel
Victor Elvira
J. Lagergren
DRL
68
17
0
22 Feb 2022
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Adrien Corenflos
Nicolas Chopin
Simo Särkkä
19
6
0
04 Feb 2022
The divide-and-conquer sequential Monte Carlo algorithm: theoretical
  properties and limit theorems
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems
Juan Kuntz
F. R. Crucinio
A. M. Johansen
19
10
0
29 Oct 2021
Data augmentation in Bayesian neural networks and the cold posterior
  effect
Data augmentation in Bayesian neural networks and the cold posterior effect
Seth Nabarro
Stoil Ganev
Adrià Garriga-Alonso
Vincent Fortuin
Mark van der Wilk
Laurence Aitchison
BDL
26
37
0
10 Jun 2021
Product-form estimators: exploiting independence to scale up Monte Carlo
Product-form estimators: exploiting independence to scale up Monte Carlo
Juan Kuntz
F. R. Crucinio
A. M. Johansen
28
10
0
23 Feb 2021
Differentiable Particle Filtering via Entropy-Regularized Optimal
  Transport
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos
James Thornton
George Deligiannidis
Arnaud Doucet
OT
41
66
0
15 Feb 2021
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