awesome-machine-learning-interpretability

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See [Algorithmic Fairness][ )

(GitHub topic)

"Code for replicating the experiments in the paper at ICML 2018, by Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan.”

"Python numba implementation of Zemel et al. 2013 "

"This is a PyTorch implementation of Multilayer Logical Perceptrons (MLLP) and Random Binarization (RB) method to learn Concept Rule Sets (CRS) for transparent classification tasks, as described in our paper: .”

"This is a PyTorch implementation of Multilayer Logical Perceptrons (MLLP) and Random Binarization (RB) method to learn Concept Rule Sets (CRS) for transparent classification tasks, as described in our paper: .”

"This accompanies the paper, by Xiyang Hu, Cynthia Rudin, and Margo Seltzer.”

See

https://github.com/albermax/innvestigate/

"PyTorch implementation of Keras already existing project: .”

"a package we (students in the ) created to make training, evaluating, and exploring neural networks flexible and easy.”

"contains source code necessary to reproduce some of the main results in the paper: , , (BMVC, 2018) [and] .”

Historical link. Merged with