lightning-uq-box

The lightning-uq-box is a PyTorch library that provides various Uncertainty Quantification (UQ) techniques for modern neural network architectures.

We hope to provide the starting point for a collaborative open source effort to make it easier for practitioners to include UQ in their workflows and
remove possible barriers of entry. Additionally, we hope this can be a pathway to more easily compare methods across UQ frameworks and potentially enhance the development of new UQ methods for neural networks.

The project is currently under active development, but we nevertheless hope for early feedback, feature requests, or contributions. Please check the Contribution Guide for further information.

The goal of this library is threefold:

1. Provide implementations for a variety of Uncertainty Quantification methods for Modern Deep Neural Networks that work with a range of neural network architectures and have different theoretical underpinnings
2. Make it easy to compare UQ methods on a given dataset
3. Focus on reproducibility of experiments with minimum boiler plate code and standardized evaluation protocols

To this end, each UQ-Method is essentially just a Lightning Module which can be used with a Lightning Data Module and a Trainer to execute training, evaluation and inference for your desired task. The library also utilizes the Lightning Command Line Interface (CLI) for better reproducibility of experiments and setting up experiments at scale.

Theory Guide

For a comprehensive document that provides more mathematical details for each method and generally forms the basis of our implementations, please see the Theory Guide. As a living document, we plan to update it as the library encompasses more methods. If you have any questions, or find typos or errors, feel free to open an issue.