Skip to content

Scripts to reproduce results within the following manuscript: Perez, I., Skalski, P., Barns-Graham, A., Wong, J. and Sutton, D. (2022) Attribution of Predictive Uncertainties in Classification Models, 38th Conference on Uncertainty in Artificial Intelligence (UAI), Eindhoven, Netherlands, 2022.

License

Notifications You must be signed in to change notification settings

Featurespace/uncertainty-attribution

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Uncertainty attributions

This is the official tensorflow implementation of the following manuscript:

Perez, I., Skalski, P., Barns-Graham, A., Wong, J. and Sutton, D. (2022) Attribution of Predictive Uncertainties in Classification Models, 38th Conference on Uncertainty in Artificial Intelligence (UAI), Eindhoven, Netherlands, 2022.

OpenReview link available here.

Contact: iker [dot] perez [at] featurespace [dot] co [dot] uk

Running the code

For the scripts to work correctly, you need to install the uncertainty_library and necessary dependencies. We recommend to do that by running

$ pip install .

in a virtual environment with python 3.8.

The scripts folder contains python scripts to train the models and reproduce results for the MNIST, FashionMNIST and CelebA datasets.

About

Scripts to reproduce results within the following manuscript: Perez, I., Skalski, P., Barns-Graham, A., Wong, J. and Sutton, D. (2022) Attribution of Predictive Uncertainties in Classification Models, 38th Conference on Uncertainty in Artificial Intelligence (UAI), Eindhoven, Netherlands, 2022.

Topics

Resources

License

Stars

Watchers

Forks

Languages