Fortuna: A Library for Uncertainty Quantification in Deep Learning
Machine Learning
2023-02-09 v1 Machine Learning
Abstract
We present Fortuna, an open-source library for uncertainty quantification in deep learning. Fortuna supports a range of calibration techniques, such as conformal prediction that can be applied to any trained neural network to generate reliable uncertainty estimates, and scalable Bayesian inference methods that can be applied to Flax-based deep neural networks trained from scratch for improved uncertainty quantification and accuracy. By providing a coherent framework for advanced uncertainty quantification methods, Fortuna simplifies the process of benchmarking and helps practitioners build robust AI systems.
Cite
@article{arxiv.2302.04019,
title = {Fortuna: A Library for Uncertainty Quantification in Deep Learning},
author = {Gianluca Detommaso and Alberto Gasparin and Michele Donini and Matthias Seeger and Andrew Gordon Wilson and Cedric Archambeau},
journal= {arXiv preprint arXiv:2302.04019},
year = {2023}
}