modelmirror

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

Enable Self-Service Operations: Give specific users access to your existing tools, services, and scripts

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

Pentest Report Generator

A Python package to assess and improve fairness of 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.

A checklist of things to look for when auditing Solidity smart contracts.

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

Papers about explainability of GNNs