interpretable
There are 21 repositories under interpretable topic.
jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
ModelOriented/modelStudio
📍 Interactive Studio for Explanatory Model Analysis
hbaniecki/adversarial-explainable-ai
💡 Adversarial attacks on explanations and how to defend them
ReproModel/repromodel
Boosting the AI research efficiency
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
navdeep-G/interpretable-ml
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Samyu0304/Improving-Subgraph-Recognition-with-Variation-Graph-Information-Bottleneck-VGIB-
graph neural networks, information theory, AI for Sciences
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
damoncrockett/wintour
Visual explanations of supervised classification models
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.
pourmand1376/yolov5
Interpretable YOLOv5