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Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Created 2018-03-14
169 commits to master branch, last one about a year ago
Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranulari...
Created 2019-06-11
539 commits to master branch, last one 9 months ago
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 ...
Created 2024-01-13
5 commits to main branch, last one 10 months ago
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Created 2022-08-03
92 commits to main branch, last one 11 months ago
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Created 2019-06-27
102 commits to master branch, last one 2 years ago