interpretable 开源项目推荐（29 个）

jphall663 /interpretable_machine_learning_with_python

用于训练可解释机器学习模型、解释机器学习模型，以及调试机器学习模型以提升准确性、减少歧视并增强安全性的技术示例。

hbaniecki /adversarial-explainable-ai

对解释的对抗攻击及如何防御

ModelOriented /modelStudio

📍 Interactive Studio for Explanatory Model Analysis

cair /pyTsetlinMachine

Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranularity, clause indexing, and literal budget

hans66hsu /nn_interpretability

Pytorch implementation of various neural network interpretability methods

pauljblazek /deepdistilling

Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms

MI2DataLab /survshap

SurvSHAP(t): Time-dependent explanations of machine learning survival models

rehmanzafar /xai-iml-sota

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

aonotas /interpretable-adv

Code for Interpretable Adversarial Perturbation in Input Embedding Space for Text, IJCAI 2018.

alinajadebarnett /iaiabl

This code repository is associated with the paper "A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography." Nature Machine Intelligence, 2021. https://www.nature.com/articles/s42256-021-00423-x

GemsLab /VoG_Graph_Summarization

Summarization of static graphs using the Minimum Description Length principle

yl3800 /IGV

This repo contains code for Invariant Grounding for Video Question Answering

Samyu0304 /Improving-Subgraph-Recognition-with-Variation-Graph-Information-Bottleneck-VGIB-

graph neural networks, information theory, AI for Sciences

navdeep-G /interpretable-ml

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

asillycat /NAG

[ICML 2026] Official implementation of Target-Oriented Pretraining Data Selection via Neuron-Activated Graph

MI2DataLab /xlungs-trustworthy-los-prediction

Trustworthy LoS Prediction Based on Multi-modal Data (AIME 2023)

sinhvt3421 /scann--material

Framework for material structure exploration

bgreenwell /ebm

Explainable Boosting Machines

pourmand1376 /yolov5

Interpretable YOLOv5

otroshi /maximal-linkability

Maximal Linkability metric to evaluate the linkability of (protected) biometric templates. Paper: "Measuring Linkability of Protected Biometric Templates using Maximal Leakage", IEEE-TIFS, 2023.