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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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 41; H. 8; S. 2008 - 2026
Hauptverfasser:	Zhang, Cheng, Butepage, Judith, Kjellstrom, Hedvig, Mandt, Stephan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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