Releases: mit-ll-responsible-ai/equine
v0.1.4
What's Changed
- Adding testing support for python 3.12 by @nukularrr in #52
- Removed timing deadline on highly variable test by @nukularrr in #53
- Protonet fixes by @RoundOffError in #61
- Other enhancements for typehints and version dependencies
- Ensured protonet backend works on the GPU by @stevenjson in #64
Full Changelog: v0.1.3...v0.1.4
Contributors
Assets 2
v0.1.4rc1
What's Changed
- Adding testing support for python 3.12 by @nukularrr in #52
- Removed timing deadline on highly variable test by @nukularrr in #53
- Protonet fixes by @RoundOffError in #61
- Other enhancements for typehints and version dependencies
Full Changelog: v0.1.3...v0.1.4rc1
Contributors
Assets 2
v0.1.3
What's Changed
*Enable concomitant visualization capability for the GP in the web-app by @nukularrr in #36
*Removed no_grad statement from GP forward method and moved model to device in gp by @gtbotkin in #42
*Various dependency updates in github actions, hypothesis, etc.
New Contributors
Full Changelog: v0.1.2...v0.1.3
Contributors
Assets 2
v0.1.3rc
What's Changed
- Enable concomitant visualization capability for the GP in the web-app by @nukularrr in #36
- Removed no_grad statement from GP forward method and moved model to device in gp by @gtbotkin in #42
- Various dependency updates in github actions, hypothesis, etc.
New Contributors
Full Changelog: v0.1.2...v0.1.3rc
Contributors
Assets 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.
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 #21
- Stevenjson/patch 1 by @nukularrr in #22
- Distance update by @RoundOffError in #28
- Auto-updated dependencies with dependabot
Full Changelog: v0.1.1...v0.1.2
Contributors
Assets 2
v0.1.1
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.
v0.1.1rc5
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: v0.1.1rc4...v0.1.1rc5