# Sylvester Normalizing Flows for Variational Inference

## TL;DR

This paper introduces Sylvester normalizing flows, a flexible and more powerful generalization of planar flows, improving variational inference by removing bottlenecks and outperforming existing flow methods on multiple datasets.

## Contribution

The paper proposes Sylvester normalizing flows, a novel class of flows that enhance flexibility and performance in variational inference beyond planar and inverse autoregressive flows.

## Key findings

- Sylvester flows outperform planar flows on several datasets.
- Sylvester flows remove the bottleneck present in planar flows.
- Sylvester flows demonstrate superior flexibility and effectiveness.

## Abstract

Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.