Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
-
Updated
Oct 7, 2024 - Python
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Evaluation and Tracking for LLM Experiments
A collection of research papers and software related to explainability in graph machine learning.
A library for graph deep learning research
Interpretability and explainability of data and machine learning models
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
moDel Agnostic Language for Exploration and eXplanation
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Generate Diverse Counterfactual Explanations for any machine learning model.
XAI - An eXplainability toolbox for machine learning
OmniXAI: A Library for eXplainable AI
👋 Xplique is a Neural Networks Explainability Toolbox
💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
H2O.ai Machine Learning Interpretability Resources
Examples of Data Science projects and Artificial Intelligence use-cases
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu
Add a description, image, and links to the explainable-ml topic page so that developers can more easily learn about it.
To associate your repository with the explainable-ml topic, visit your repo's landing page and select "manage topics."