A game theoretic approach to explain the output of any machine learning model.
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Updated
May 16, 2024 - Jupyter Notebook
A game theoretic approach to explain the output of any machine learning model.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
A collection of infrastructure and tools for research in neural network interpretability.
A curated list of awesome responsible machine learning resources.
Model interpretability and understanding for PyTorch
StellarGraph - Machine Learning on Graphs
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Algorithms for explaining machine learning models
[ICCV 2017] Torch code for Grad-CAM
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…
A collection of research materials on explainable AI/ML
moDel Agnostic Language for Exploration and eXplanation
Public facing deeplift repo
XAI - An eXplainability toolbox for machine learning
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