2017 AdvancesinVariationalInference

• (Zhang et al., 2017) ⇒ Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, and Stephan Mandt. (2017). “Advances in Variational Inference.” In: arXiv preprint arXiv:1711.05597.

Subject Headings: Variational Bayes Inference.

Cited By

• http://scholar.google.com/scholar?q=%222017%22+Advances+in+Variational+Inference

Quotes

Abstract

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 distribution by solving an optimization problem. This approach has been successfully used in various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 AdvancesinVariationalInference	Cheng Zhang Judith Butepage Hedvig Kjellstrom Stephan Mandt			Advances in Variational Inference