• Related Project

Why Should I Trust You?": Explaining the Predictions of Any Classifier"


Institutions :
U of Washington
Authors :
M. T. Ribeiro, S. Singh, S. and C. Guestrin
Publication :
Why Should I Trust You?: Explaining the Predictions of Any Classifier, KDD, 2016
Source Code :
https://github.com/marcotcr/lime


Explaining Recurrent Neural Network Predictions in Sentiment Analysis


Institutions :
Fraunhofer, TU Berlin, Korea University, Max
Authors :
L. Arras, G. Montavon, K-R. Müller and W. Samek
Publication :
Explaining Recurrent Neural Network Predictions in Sentiment Analysis, EMNLP, 2017
Source Code :
https://github.com/ArrasL/LRP_for_LSTM


Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model


Institutions :
MIT, U of Washington, Columbia
Authors :
B. Letham, C. Rudin, T. McCormick and D. Madigan
Publication :
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model, Annals of Applied Statistics, 2015
Source Code :
https://github.com/nlarusstone/corels


Principles of Explanatory Debugging to Personalize Interactive Machine Learning


Institutions :
Oregon State, City University London
Authors :
T. Kulesza, M. Burnett, W-K. Wong and S. Stumpf
Publication :
Principles of Explanatory Debugging to Personalize Interactive Machine Learning, IUI, 2015
Source Code :
https://github.com/fflewddur/IMLPlayground