论文《Advances in Variational Inference》摘要：
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.
