# Quantized Variational Inference

arXiv: https://arxiv.org/abs/2011.02271 · HTML: https://mlsanity.com/paper/2011.02271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

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

- [Reproducible, incremental representation learning with Rosetta VAE](https://mlsanity.com/paper/2201.05206/) (2022)

## Abstract (arXiv)

We present Quantized Variational Inference, a new algorithm for Evidence Lower Bound maximization. We show how Optimal Voronoi Tesselation produces variance free gradients for ELBO optimization at the cost of introducing asymptotically decaying bias. Subsequently, we propose a Richardson extrapolation type method to improve the asymptotic bound. We show that using the Quantized Variational Inference framework leads to fast convergence for both score function and the reparametrized gradient estimator at a comparable computational cost. Finally, we propose several experiments to assess the performance of our method and its limitations.