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The predictive power of neural networks often costs model interpretability.
Histograms of oriented gradients for human detection
Dalal, Navneet and Triggs, Bill · 2005
Earlier work this paper cites.
Efficient learning of sparse representations with an energy-based model
Ranzato, Marc’Aurelio, Poultney, Christopher, Chopra, Sumit, and LeCun, Yann · 2006
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How to explain individual classification decisions
Baehrens, David, Schroeter, Timon, Harmeling, Stefan, Kawanabe, Motoaki, Hansen, Katja, and MÞller, Klaus-Robert · 2010
Earlier work this paper cites.
Adaptive deconvolutional networks for mid and high level feature learning
Zeiler, Matthew D, Taylor, Graham W, and Fergus, Rob · 2011
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Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2014
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Visualizing and understanding convolutional networks
Zeiler, Matthew D and Fergus, Rob · 2014
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The mythos of model interpretability
Lipton, Zachary C · 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
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Samek, Wojciech, Binder, Alexander, Montavon, Grégoire, Lapuschkin, Sebastian, and Müller, Klaus-Robert · 2016
Cited alongside, same era.
Not just a black box: Learning important features through propagating activation differences
Shrikumar, Avanti, Greenside, Peyton, Shcherbina, Anna, and Kundaje, Anshul · 2016
Cited alongside, same era.
Contextual explanation networks
Al-Shedivat, Maruan, Dubey, Avinava, and Xing, Eric P · 2017
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Opportunities and obstacles for deep learning in biology and medicine
Ching, Travers, Himmelstein, Daniel S, Beaulieu-Jones, Brett K, Kalinin, Alexandr A, Do, Brian T, Way, Gregory P, Ferrero, Enrico, Agapow, Paul-Michael, Xie, Wei, Rosen, Gail L, et al · 2017
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Understanding black-box predictions via influence functions
Koh, Pang Wei and Liang, Percy · 2017
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Vision-based navigation and deep-learning explanation for autonomy
Konam, Sandeep · 2017
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Unsupervised sentiment neuron, 2017
Radford, Alec, Sutskever, Ilya, Jozefowicz, Rafal, Clark, Jack, and Brockman, Greg · 2017
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