Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world datasets and workflows.
An interpretable framework for inferring nonlinear multivariate Granger causality based on self-explaining neural networks.
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
A list of research papers of explainable machine learning.
Comprehensible Convolutional Neural Networks via Guided Concept Learning
Code for Surgical Skill Assessment via Video Semantic Aggregation (MICCAI 2022)
A Python implementation of Word Mover's Distance that decomposes document level WMD into word level WMD for interpretable sociocultural NLP.
Explainable Boosting Machines
Visual Intelligence is a desktop app that extracts text from images and PDFs in Turkish and English using Tesseract OCR. It offers advanced features like text summarization, keyword extraction, table and QR/barcode detection. The app has a modern, user-friendly interface built with TailwindCSS and Vanilla JS.
Add a description, image, and links to the interpretable-models topic page so that developers can more easily learn about it.
To associate your repository with the interpretable-models topic, visit your repo's landing page and select "manage topics."