This is the Windows app named Uncertainty Baselines whose latest release can be downloaded as uncertainty-baselinessourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS
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Uncertainty Baselines
DESCRIPTION
Uncertainty Baselines is a collection of strong, well-documented training pipelines that make it straightforward to evaluate predictive uncertainty in modern machine learning models. Rather than offering toy scripts, it provides end-to-end recipes—data input, model architectures, training loops, evaluation metrics, and logging—so results are comparable across runs and research groups. The library spans canonical modalities and tasks, from image classification and NLP to tabular problems, with baselines that cover both deterministic and probabilistic approaches. Techniques include deep ensembles, Monte Carlo dropout, temperature scaling, stochastic variational inference, heteroscedastic heads, and out-of-distribution detection workflows. Each baseline emphasizes reproducibility: fixed seeds, standard splits, and strong metrics such as calibration error, AUROC for OOD, and accuracy under shift.
Features
- End-to-end, reproducible pipelines for uncertainty evaluation
- Coverage of ensembles, MC dropout, SVI, and calibration methods
- Standardized metrics for OOD detection and calibration quality
- Baselines across vision, language, and tabular tasks
- Clear configuration files and logging for fair comparisons
- Strong defaults that can be extended for new research ideas
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/uncertainty-baselines.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
