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#

interpretability

Here are 766 public repositories matching this topic...

A game theoretic approach to explain the output of any machine learning model.

  • UpdatedMay 1, 2025
  • Jupyter Notebook

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

  • UpdatedApr 7, 2025
  • Python

Model interpretability and understanding for PyTorch

  • UpdatedMay 2, 2025
  • Python

A collection of infrastructure and tools for research in neural network interpretability.

  • UpdatedFeb 6, 2023
  • Jupyter Notebook

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

  • UpdatedApr 25, 2025
  • Jupyter Notebook

Algorithms for explaining machine learning models

  • UpdatedApr 4, 2025
  • Python
torch-cam

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)

  • UpdatedMay 2, 2025
  • Python

A JAX research toolkit for building, editing, and visualizing neural networks.

  • UpdatedApr 26, 2025
  • Python

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.

  • UpdatedFeb 7, 2025
  • TypeScript

A collection of research materials on explainable AI/ML

  • UpdatedMar 21, 2025
  • Markdown

Stanford NLP Python library for Representation Finetuning (ReFT)

  • UpdatedFeb 6, 2025
  • Python
imodelsDALEX

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

  • UpdatedAug 30, 2023
  • Jupyter Notebook

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