shapash

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

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

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Visualization toolkit for neural networks in PyTorch! Demo -->

Papers about explainability of GNNs

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet