Advances in Variational Inference

Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 41; číslo 8; s. 2008 - 2026
Hlavní autori:	Zhang, Cheng, Butepage, Judith, Kjellstrom, Hedvig, Mandt, Stephan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms approximate Bayesian inference Bayes methods Bayesian analysis Computational modeling Hidden Markov models Inference inference networks Machine learning Optimization Probabilistic logic Probabilistic models reparameterization gradients scalable inference Stochastic processes structured variational approximations Variational inference
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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