Learning variational autoencoders via MCMC speed measures

Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximising an evidence lower bound. There has been much progress in improving the expressiveness of the variational distribution to obtain tighter variational bounds and increased gener...

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Veröffentlicht in:	Statistics and computing Jg. 34; H. 5
Hauptverfasser:	Hirt, Marcel, Kreouzis, Vasileios, Dellaportas, Petros
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2024 Springer Nature B.V
Schlagworte:	Artificial Intelligence Computer Science Lower bounds Markov chains Monte Carlo simulation Optimization Original Paper Probability and Statistics in Computer Science Statistical Theory and Methods Statistics and Computing/Statistics Programs Adaptive MCMC MALA Generative models Hierarchical models HMC
ISSN:	0960-3174, 1573-1375
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Abstract	Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximising an evidence lower bound. There has been much progress in improving the expressiveness of the variational distribution to obtain tighter variational bounds and increased generative performance. Whilst previous work has leveraged Markov chain Monte Carlo methods for constructing variational densities, gradient-based methods for adapting the proposal distributions for deep latent variable models have received less attention. This work suggests an entropy-based adaptation for a short-run metropolis-adjusted Langevin or Hamiltonian Monte Carlo (HMC) chain while optimising a tighter variational bound to the log-evidence. Experiments show that this approach yields higher held-out log-likelihoods as well as improved generative metrics. Our implicit variational density can adapt to complicated posterior geometries of latent hierarchical representations arising in hierarchical VAEs.
AbstractList	Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximising an evidence lower bound. There has been much progress in improving the expressiveness of the variational distribution to obtain tighter variational bounds and increased generative performance. Whilst previous work has leveraged Markov chain Monte Carlo methods for constructing variational densities, gradient-based methods for adapting the proposal distributions for deep latent variable models have received less attention. This work suggests an entropy-based adaptation for a short-run metropolis-adjusted Langevin or Hamiltonian Monte Carlo (HMC) chain while optimising a tighter variational bound to the log-evidence. Experiments show that this approach yields higher held-out log-likelihoods as well as improved generative metrics. Our implicit variational density can adapt to complicated posterior geometries of latent hierarchical representations arising in hierarchical VAEs.
ArticleNumber	164
Author	Hirt, Marcel Kreouzis, Vasileios Dellaportas, Petros
Author_xml	– sequence: 1 givenname: Marcel surname: Hirt fullname: Hirt, Marcel organization: School of Social Sciences and School of Physical and Mathematical Sciences, Nanyang Technological University – sequence: 2 givenname: Vasileios surname: Kreouzis fullname: Kreouzis, Vasileios organization: Department of Statistical Science, University College London – sequence: 3 givenname: Petros surname: Dellaportas fullname: Dellaportas, Petros email: p.dellaportas@ucl.ac.uk organization: Department of Statistical Science, University College London, Department of Statistics, Athens University of Economics and Business
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Cites_doi	10.1111/1467-9868.00196 10.1214/088342307000000014 10.24963/ijcai.2017/273 10.1137/21M1450604 10.1007/978-3-030-58539-6_22 10.1007/s10107-007-0149-x 10.1162/NECO_a_00142 10.1017/S0962492902000144 10.1109/CVPR52688.2022.01042 10.1609/aaai.v31i1.10902 10.3150/18-BEJ1083 10.1109/ICCV51070.2023.00393 10.3390/e23030269 10.1017/S0962492917000101 10.1109/FOCS57990.2023.00134 10.1111/j.1467-9868.2009.00736.x
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Snippet	Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximising an evidence lower bound. There...
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Title	Learning variational autoencoders via MCMC speed measures
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