uncertainty-toolbox
AIX360
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| uncertainty-toolbox | AIX360 | |
|---|---|---|
| 1 | 2 | |
| 1,711 | 1,527 | |
| 3.1% | 2.7% | |
| 10.0 | 8.2 | |
| over 1 year ago | about 2 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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
AIX360
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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[R] Explaining the Explainable AI: A 2-Stage Approach - Link to a free online lecture by the author in comments
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques https://arxiv.org/abs/1909.03012 https://github.com/Trusted-AI/AIX360
What are some alternatives?
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
TorchDrift - Drift Detection for your PyTorch Models
explainable-cnn - 📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
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.
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
uq-vae - Solving Bayesian Inverse Problems via Variational Autoencoders