PaperSummaries

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

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

[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

Visualization tool for Graph Neural Networks

This is an open-source version of the representation engineering framework for stopping harmful outputs or hallucinations on the level of activations. 100% free, self-hosted and open-source.

GraphXAI: Resource to support the development and evaluation of GNN explainers