Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 1, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Generate Diverse Counterfactual Explanations for any machine learning model.
moDel Agnostic Language for Exploration and eXplanation
[HELP REQUESTED] Generalized Additive Models in Python
Interesting resources related to XAI (Explainable Artificial Intelligence)
H2O.ai Machine Learning Interpretability Resources
PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
OmniXAI: A Library for eXplainable AI
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
🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI )
💡 Adversarial attacks on explanations and how to defend them
PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
📍 Interactive Studio for Explanatory Model Analysis
🕵️♂️ Interpreting Convolutional Neural Network (CNN) Results.
Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.
Concept Bottleneck Models, ICML 2020
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