A curated list of awesome responsible machine learning resources.
-
Updated
May 14, 2024
A curated list of awesome responsible machine learning resources.
A PyTorch implementation of constrained optimization and modeling techniques
Fit interpretable models. Explain blackbox machine learning.
Distributed High-Performance Symbolic Regression in Julia
Model interpretability and understanding for PyTorch
High-Performance Symbolic Regression in Python and Julia
Explainable Machine Learning in Survival Analysis
Random Planted Forest
JAX-based Model Explanation and Interpretation Library
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
An Open-Source Library for the interpretability of time series classifiers
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
Measuring Biases in Masked Language Models for PyTorch Transformers. Support for multiple social biases and evaluation measures.
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
XAI-Tris
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI )
Decision Trees to understand CNNs. Project for Neural Networks 2020 course at Sapienza.
Explaining Model Behavior with Global Causal Analysis
Optimal Sparse Decision Trees
Add a description, image, and links to the interpretable-ml topic page so that developers can more easily learn about it.
To associate your repository with the interpretable-ml topic, visit your repo's landing page and select "manage topics."