Equine

The goal of this package is to make it simple to add modern uncertainty quantification (UQ) techniques to existing PyTorch models to produce label predictions with calibrated probabilities and out-of-distribution indicators.

What's Changed
Overall: two minor bugfixes, testing coverage increased, improved type hinting, and more comprehensive CI tools and actions.

* Feature/beartype by nukularrr in https://github.com/mit-ll-responsible-ai/equine/pull/21
* Stevenjson/patch 1 by nukularrr in https://github.com/mit-ll-responsible-ai/equine/pull/22
* Distance update by RoundOffError in https://github.com/mit-ll-responsible-ai/equine/pull/28
* Auto-updated dependencies with dependabot

**Full Changelog**: https://github.com/mit-ll-responsible-ai/equine/compare/v0.1.1...v0.1.2

EQUI(NE)^2 (equine): Establishing Quantified Uncertainty for Neural Networks

The goal of this package is to make it simple to add modern uncertainty quantification (UQ) techniques to existing PyTorch models to produce label predictions with calibrated probabilities and out-of-distribution indicators.

EQUI(NE)^2 (equine): Establishing Quantified Uncertainty for Neural Networks

The goal of this package is to make it simple to add modern uncertainty quantification (UQ) techniques to existing PyTorch models to produce label predictions with calibrated probabilities and out-of-distribution indicators.

What's Changed
* Added the Zenodo-linked DOI via GitHub integration.
* Added automatic versioning via `setuptools_scm`

Full Changelog: https://github.com/mit-ll-responsible-ai/equine/compare/v0.1.1rc4...v0.1.1rc5

Minor changes to add Zenodo DOI

What's Changed
* More documentation updates
* Better test coverage

Testing PyPI again

ScatterUQ-VIS-2023-Data
This release contains our raw data, analysis script, and output data that we used to publish our short paper to VIS 2023

ScatterUQ-VIS-2023-Data
This release contains our raw data, analysis script, and output data that we used to publish our short paper to VIS 2023