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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Vydané v:	Statistics and computing Ročník 34; číslo 5
Hlavní autori:	Hirt, Marcel, Kreouzis, Vasileios, Dellaportas, Petros
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.10.2024 Springer Nature B.V
Predmet:	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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