# Variational Inference via Transformations on Distributions

arXiv: https://arxiv.org/abs/1707.02510 · HTML: https://mlsanity.com/paper/1707.02510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Boosting Variational Inference With Locally Adaptive Step-Sizes](https://mlsanity.com/paper/2105.09240/) (2021)

## Abstract (arXiv)

Variational inference methods often focus on the problem of efficient model optimization, with little emphasis on the choice of the approximating posterior. In this paper, we review and implement the various methods that enable us to develop a rich family of approximating posteriors. We show that one particular method employing transformations on distributions results in developing very rich and complex posterior approximation. We analyze its performance on the MNIST dataset by implementing with a Variational Autoencoder and demonstrate its effectiveness in learning better posterior distributions.