# A Comparison of PDF Projection with Normalizing Flows and SurVAE

arXiv: https://arxiv.org/abs/2311.14412 · HTML: https://mlsanity.com/paper/2311.14412/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 24 Nov 2023.

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

- [Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs](https://mlsanity.com/paper/2401.11199/) (2024)

## Abstract (arXiv)

Normalizing flows (NF) recently gained attention as a way to construct generative networks with exact likelihood calculation out of composable layers. However, NF is restricted to dimension-preserving transformations. Surjection VAE (SurVAE) has been proposed to extend NF to dimension-altering transformations. Such networks are desirable because they are expressive and can be precisely trained. We show that the approaches are a re-invention of PDF projection, which appeared over twenty years earlier and is much further developed.