RoCourseNet

A library for debugging/inspecting machine learning classifiers and explaining their predictions

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

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

[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.

Leave One Feature Out Importance

Code, exercises and tutorials of my personal blog ! ?

Diffusion attentive attribution maps for interpreting Stable Diffusion.