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MetFlow: A New Efficient Method for Bridging the Gap between Markov
  Chain Monte Carlo and Variational Inference

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference

27 February 2020
Achille Thin
Nikita Kotelevskii
Jean-Stanislas Denain
Léo Grinsztajn
Alain Durmus
Maxim Panov
Eric Moulines
    BDL
ArXiv (abs)PDFHTML

Papers citing "MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference"

13 / 13 papers shown
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance SamplingConference on Uncertainty in Artificial Intelligence (UAI), 2024
Jian Xu
Shian Du
Junmei Yang
Qianli Ma
Delu Zeng
John Paisley
BDL
621
3
0
13 Aug 2024
Predictive Uncertainty Quantification via Risk Decompositions for
  Strictly Proper Scoring Rules
Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules
Nikita Kotelevskii
Maxim Panov
PERUQCVUD
479
3
0
16 Feb 2024
Learning variational autoencoders via MCMC speed measures
Learning variational autoencoders via MCMC speed measuresStatistics and computing (Stat. Comput.), 2023
Marcel Hirt
Vasileios Kreouzis
P. Dellaportas
BDLDRL
230
2
0
26 Aug 2023
Optimization of Annealed Importance Sampling Hyperparameters
Optimization of Annealed Importance Sampling Hyperparameters
Shirin Goshtasbpour
Fernando Perez-Cruz
395
1
0
27 Sep 2022
The Free Energy Principle for Perception and Action: A Deep Learning
  Perspective
The Free Energy Principle for Perception and Action: A Deep Learning Perspective
Pietro Mazzaglia
Tim Verbelen
Ozan Çatal
Bart Dhoedt
DRLAI4CE
335
39
0
13 Jul 2022
Multimodal Maximum Entropy Dynamic Games
Multimodal Maximum Entropy Dynamic Games
Oswin So
Kyle Stachowicz
Evangelos A. Theodorou
352
12
0
30 Jan 2022
Annealed Flow Transport Monte Carlo
Annealed Flow Transport Monte CarloInternational Conference on Machine Learning (ICML), 2021
Michael Arbel
A. G. Matthews
Arnaud Doucet
421
96
0
15 Feb 2021
Nonreversible MCMC from conditional invertible transforms: a complete
  recipe with convergence guarantees
Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees
Achille Thin
Nikita Kotolevskii
Christophe Andrieu
Alain Durmus
Eric Moulines
Maxim Panov
294
6
0
31 Dec 2020
A general perspective on the Metropolis-Hastings kernel
A general perspective on the Metropolis-Hastings kernel
Christophe Andrieu
Anthony Lee
Samuel Livingstone
340
28
0
29 Dec 2020
Accelerating MCMC algorithms through Bayesian Deep Networks
Accelerating MCMC algorithms through Bayesian Deep Networks
Héctor J. Hortúa
Riccardo Volpi
D. Marinelli
Luigi Malagò
BDL
146
14
0
29 Nov 2020
Measure Transport with Kernel Stein Discrepancy
Measure Transport with Kernel Stein Discrepancy
Matthew A. Fisher
T. Nolan
Matthew M. Graham
D. Prangle
Chris J. Oates
OT
345
15
0
22 Oct 2020
Qualitative Analysis of Monte Carlo Dropout
Qualitative Analysis of Monte Carlo Dropout
Ronald Seoh
UQCVBDL
149
39
0
03 Jul 2020
Constraining the Reionization History using Bayesian Normalizing Flows
Constraining the Reionization History using Bayesian Normalizing Flows
Héctor J. Hortúa
Luigi Malagò
Riccardo Volpi
BDL
288
21
0
14 May 2020
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