SYMPOL

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

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

Evaluation and Tracking for LLM Experiments

A collection of research papers and software related to explainability in graph machine learning.

A library for graph deep learning research

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Interpretability and explainability of data and machine learning models

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

Explainability for Vision Transformers