… variational parameters. We propose a hybrid approach, to use AVI to initialize the variational
parameters and run stochastic variational … This semi-amortized approach enables the use of …

… Our method is motivated by the premise of the semi-amortized inference (SAVI), ie, refining
the variational posterior to further reduce the difference from the true posterior. However, …

… Whereas the semi-amortized approaches perform extra SVI … faster than that of semiamortized
methods, accomplished by a … -of-the-art semi-amortized approaches and even the high-…

… s variational posterior distributions from the true posteriors can be naturally regarded as random
noise. Whereas the semi-amortized … than that of semi-amortized methods, accomplished …

… The recognition network in deep latent variable models such as variational autoencoders
(VAEs) relies on amortized inference for efficient posterior approximation that can scale up to …

… 2014) using the standard fully-factorized Gaussian assumption, (2) SemiAmortized VAE (SA-VAE)
which extends the VAE using gradient-based updates of variational parameters (Kim …

… posits a variational family … AutoEncoder to learn unsupervised representations from the
point cloud data. To add consistency regularization, we first substitute the AutoEncoder …

… , Variational Autoencoder (VAE) approximates the posterior of latent variables based on
amortized variational … Semiamortized variational autoencoders. In ICML, 2018. [Kingma and Ba, …

… Variational Auto-encoders (VAEs) are deep generative latent variable models that transform
simple … Variational auto-encoders are widely used by practitioners due to the ease of their …

… The resulting semi-amortized approach empirically avoided collapse and obtained better
ELBO. However, because of the costly instancespecific local inference steps, the new method …