uncertainty-toolbox
uq-vae
Our great sponsors
| uncertainty-toolbox | uq-vae | |
|---|---|---|
| 1 | 1 | |
| 1,711 | 5 | |
| 3.1% | - | |
| 10.0 | 5.7 | |
| over 1 year ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
uncertainty-toolbox
uq-vae
What are some alternatives?
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
fortuna - A Library for Uncertainty Quantification.
TorchDrift - Drift Detection for your PyTorch Models
GPflow - Gaussian processes in TensorFlow
pytea - PyTea: PyTorch Tensor shape error analyzer
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
ipme - An interactive visualization tool that transforms probabilistic programming models into an "Interactive Probabilistic Models Explorer".
WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks
explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
cockpit - Cockpit: A Practical Debugging Tool for Training Deep Neural Networks
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models