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Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces.
Comprehensible classification models: A position paper
Freitas, Alex A · 1931
Earlier work this paper cites.
The magical number seven, plus or minus two: Some limits on our capacity for processing information, 1956
Miller, George · 1956
Earlier work this paper cites.
Extracting tree-structured representations of trained networks
Craven, Mark W and Shavlik, Jude W · 1996
Earlier work this paper cites.
Learning from labeled features using generalized expectation criteria
Druck, Gregory, Mann, Gideon, and McCallum, Andrew · 2008
Earlier work this paper cites.
Modeling annotators: A generative approach to learning from annotator rationales
Zaidan, Omar F. and Eisner, Jason · 2008
Earlier work this paper cites.
How to explain individual classification decisions
Baehrens, David, Schroeter, Timon, Harmeling, Stefan, Kawanabe, Motoaki, Hansen, Katja, and Müller, Klaus-Robert · 2010
Earlier work this paper cites.
An efficient explanation of individual classifications using game theory
Strumbelj, Erik and Kononenko, Igor · 2010
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
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Falling rule lists
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Wieting, John, Bansal, Mohit, Gimpel, Kevin, and Livescu, Karen · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Xu, Kelvin, Ba, Jimmy, Kiros, Ryan, Cho, Kyunghyun, Courville, Aaron, Salakhutdinov, Ruslan, Zemel, Richard, and Bengio, Yoshua · 2015
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Letham, Benjamin, Rudin, Cynthia, McCormick, Tyler H., and Madigan, David · 2015
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Injecting logical background knowledge into embeddings for relation extraction
Rocktaschel, Tim, Singh, Sameer, and Riedel, Sebastian · 2015
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Krause, Josua, Perer, Adam, and Ng, Kenney · 2016
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“why should I trust you?”: Explaining the predictions of any classifier
Ribeiro, Marco Tulio, Singh, Sameer, and Guestrin, Carlos · 2016
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