Popular GitHub Repositories for Explorable Explanations

Enhancing code comprehension with AI-driven explanations and a secure code snippet repository.

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Algorithms for explaining machine learning models

Official Repo for "TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding"

AI-powered tools to enhance Anki flashcards with explanations, mnemonics, illustrations, and adaptive learning for medical school and beyond

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

Use AI-powered code edits, explanations, code generation, error diagnosis, and chat in Visual Studio Code with the official OpenAI API.

Generate large-scale explorable 3D scene from a single image or text prompt.

A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.

? Adversarial attacks on explanations and how to defend them

Diffusers-Interpret ?????♀?: Model explainability for ? Diffusers. Get explanations for your generated images.

Speech Emotion Classification with novel Parallel CNN-Transformer model built with PyTorch, plus thorough explanations of CNNs, Transformers, and everything in between

? PyTorch based visualization package for generating layer-wise explanations for CNNs.

Repository for the Explainable Deep One-Class Classification paper

TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (ACL 2020)

Pytorch Implementation of recent visual attribution methods for model interpretability

Collection of NLP model explanations and accompanying analysis tools

On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines