# PaiDEs_Nflows_Base
A library to train Normalizing Flow Ensembles (Nflows Base) using a fixed set of dropout masks and combine them with Pairwise-Distance Estimators (PaiDEs) for the ultimate in aleatoric expressiveness with the most efficient epistemic uncertainty estimation. We compare our method to Monte Carlo (MC) Estimators on an array of active learning settings. Our results show how to estimate uncertainty for NFs in efficient manner. Please refer to PAPER TITLE/LINK for more information. In addition, we compared our method on set of active learning experiements utilizing Probabilistic Network Ensembles (PNEs).


# Installation
Code was run on python 3.8 and requires installation of the packages in requirements.txt

`pip install -r requirements.txt`

Please note that an altered version of https://github.com/bayesiains/nflows and https://github.com/openai/gym were used. They are contained in the repository and can be installed via the following commands:

```cd nflows
pip install -e .
cd gym
pip install -e .
```


# Experiments

Please refer to example_1d.txt and example_multid.txt for how to run experiments
