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While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate.
Likelihood ratio gradient estimation for stochastic systems
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Gradient-based learning applied to document recognition
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Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning
Ishibuchi, H. and Nojima, Y. (2007) · 2007
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R. (2014) · 2014
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Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2014) · 2015
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Predicting effects of noncoding variants with deep learning-based sequence model
Zhou, J. and Troyanskaya, O. G. (2015) · 2015
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" why should i trust you?" explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
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Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y. (2017) · 2017
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B. (2017) · 2017
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Interpretable & explorable approximations of black box models
Lakkaraju, H., Kamar, E., Caruana, R., and Leskovec, J. (2017) · 2017
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The mythos of model interpretability
Lipton, Z. C. (2017) · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S. I. (2017) · 2017
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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Maddison, C. J., Mnih, A., and Teh, Y. W. (2016) · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. M. (2017) · 2017
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Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M. (2017) · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B. (2018) · 2018
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Learning to explain: An information-theoretic perspective on model interpretation
Chen, J., Song, L., Wainwright, M., and Jordan, M. (2018) · 2018
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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., and Oermann, E. K. (2018) · 2018
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Learning important features through propagating activation differences
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Mastering the game of go without human knowledge
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Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
Tucker, G., Mnih, A., Maddison, C. J., Lawson, J., and Sohl-Dickstein, J. (2017) · 2017
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A benchmark for interpretability methods in deep neural networks
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Cxplain: Causal explanations for model interpretation under uncertainty
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