# Recursive Inference for Variational Autoencoders

@article{Kim2020RecursiveIF, title={Recursive Inference for Variational Autoencoders}, author={Minyoung Kim and V. Pavlovic}, journal={ArXiv}, year={2020}, volume={abs/2011.08544} }

Inference networks of traditional Variational Autoencoders (VAEs) are typically amortized, resulting in relatively inaccurate posterior approximation compared to instance-wise variational optimization. Recent semi-amortized approaches were proposed to address this drawback; however, their iterative gradient update procedures can be computationally demanding. To address these issues, in this paper we introduce an accurate amortized inference algorithm. We propose a novel recursive mixture… Expand

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Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach

- Computer Science
- ArXiv
- 2021

This paper considers a random inference model, where the mean and variance functions of the variational posterior as random Gaussian processes (GP) so that the deviation of the VAE’s amortized posterior distribution from the true posterior can be regarded as random noise, which allows us to take into account the uncertainty in posterior approximation in a principled manner. Expand

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