Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models
Abstract
Normalizing flows are powerful invertible probabilistic models that can be used to translate two probability distributions, in a way that allows us to efficiently track the change of probability density. However, to trade for computational efficiency in sampling and in evaluating the log-density, special parameterization designs have been proposed at the cost of representational expressiveness. In this work, we propose to use ODEs as a framework to establish universal approximation theory for certain families of flow-based models.
Cite
Text
Huang et al. "Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models." ICLR 2020 Workshops: DeepDiffEq, 2020.Markdown
[Huang et al. "Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models." ICLR 2020 Workshops: DeepDiffEq, 2020.](https://mlanthology.org/iclrw/2020/huang2020iclrw-solving/)BibTeX
@inproceedings{huang2020iclrw-solving,
title = {{Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models}},
author = {Huang, Chin-Wei and Dinh, Laurent and Courville, Aaron},
booktitle = {ICLR 2020 Workshops: DeepDiffEq},
year = {2020},
url = {https://mlanthology.org/iclrw/2020/huang2020iclrw-solving/}
}