AIX360
loss-landscape
AIX360 | loss-landscape | |
---|---|---|
2 | 2 | |
1,533 | 2,642 | |
2.0% | - | |
8.2 | 0.0 | |
2 months ago | about 2 years ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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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.
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
loss-landscape
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
-
[D] Visualizing loss surface in input space
Code for https://arxiv.org/abs/1712.09913 found: https://github.com/tomgoldstein/loss-landscape
What are some alternatives?
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.
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cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
cockpit - Cockpit: A Practical Debugging Tool for Training Deep Neural Networks