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Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models.
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Explaining instance classifications with interactions of subsets of feature values
Erik Štrumbelj, Igor Kononenko, and M Robnik Šikonja · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Quantifying causal influences
Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, and Bernhard Schölkopf · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
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Capturing the essence: Towards the automated generation of transparent behavior models
Patrick Schwab and Helmut Hlavacs · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Diederik Kingma and Jimmy Ba · 2015
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The mythos of model interpretability
Zachary C Lipton · 2016
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Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Understanding neural networks through representation erasure
Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Interpreting blackbox models via model extraction
Osbert Bastani, Carolyn Kim, and Hamsa Bastani · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
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Detecting statistical interactions from neural network weights
Michael Tsang, Dehua Cheng, and Yan Liu · 2017
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Understanding black-box predictions via influence functions
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Jiwei Li, Will Monroe, and Dan Jurafsky · 2016
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Interpretable deep models for ICU outcome prediction
Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Rethinking the Inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2017
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Pang Wei Koh and Percy Liang · 2017
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Lukasz Kaiser, Aidan N Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, and Jakob Uszkoreit · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Learning to explain: An information-theoretic perspective on model interpretation
Jianbo Chen, Le Song, Martin J Wainwright, and Michael I Jordan · 2018
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Explaining deep learning models–a bayesian non-parametric approach
Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, and Lin Lin · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2018
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Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Patrick Schwab, Djordje Miladinovic, and Walter Karlen · 2019
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PhoneMD: Learning to diagnose Parkinson’s disease from smartphone data
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Why should you trust my interpretation? Understanding uncertainty in LIME predictions
Hui Fen, Kuangyan Song, Madeilene Udell, Yiming Sun, Yujia Zhang, et al · 2019
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
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Neural network attributions: A causal perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, and Vineeth N Balasubramanian · 2019
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What to expect of classifiers? Reasoning about logistic regression with missing features
Pasha Khosravi, Yitao Liang, YooJung Choi, and Guy Van den Broeck · 2019
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