Fit interpretable models. Explain blackbox machine learning.
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Updated
Sep 6, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
moDel Agnostic Language for Exploration and eXplanation
Generate Diverse Counterfactual Explanations for any machine learning model.
PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
[HELP REQUESTED] Generalized Additive Models in Python
OmniXAI: A Library for eXplainable AI
Interesting resources related to XAI (Explainable Artificial Intelligence)
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
H2O.ai Machine Learning Interpretability Resources
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
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI )
📍 Interactive Studio for Explanatory Model Analysis
💡 Adversarial attacks on explanations and how to defend them
PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
🕵️♂️ Interpreting Convolutional Neural Network (CNN) Results.
Concept Bottleneck Models, ICML 2020
🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
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