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Saliency methods are used extensively to highlight the importance of input features in model predictions.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gaussian processes in machine learning
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Polynomial calculation of the shapley value based on sampling
Javier Castro, Daniel Gómez, and Juan Tejada · 2009
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÞller · 2010
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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The wu-minn human connectome project: an overview
David C Van Essen, Stephen M Smith, Deanna M Barch, Timothy EJ Behrens, Essa Yacoub, Kamil Ugurbil, Wu-Minn HCP Consortium, et al · 2013
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Machine learning, automated suspicion algorithms, and the fourth amendment
Michael L Rich · 2016
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Predicting the future—big data, machine learning, and clinical medicine
Ziad Obermeyer and Ezekiel J Emanuel · 2016
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Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne · 2016
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey Hinton · 2017
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 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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Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Learning how to explain neural networks: Patternnet and patternattribution
Pieter-Jan Kindermans, Kristof T Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, and Sven Dähne · 2017
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Clinical intervention prediction and understanding using deep networks
Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi · 2017
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Temporal convolutional networks for action segmentation and detection
Colin Lea, Michael Flynn, Rene Vidal, Austin Reiter, and Gregory Hager · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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The doctor just won’t accept that!
Zachary C Lipton · 2017
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The mythos of model interpretability
Zachary C Lipton · 2018
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Alexander Levine, Sahil Singla, and Soheil Feizi · 2019
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
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The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2019
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Understanding impacts of high-order loss approximations and features in deep learning interpretation
Sahil Singla, Eric Wallace, Shi Feng, and Soheil Feizi · 2019
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Input-cell attention reduces vanishing saliency of recurrent neural networks
Aya Abdelsalam Ismail, Mohamed Gunady, Luiz Pessoa, Hector Corrada Bravo, and Soheil Feizi · 2019
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What clinicians want: contextualizing explainable machine learning for clinical end use
Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden, and Anna Goldenberg · 2019
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What went wrong and when? instance-wise feature importance for time-series models
Sana Tonekaboni, Shalmali Joshi, David Duvenaud, and Anna Goldenberg · 2020
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Explaining an increase in predicted risk for clinical alerts
Michaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew Dai, Michael Howell, Greg Corrado, Claire Cui, and Moritz Hardt · 2020
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Interpretable Machine Learning
Christoph Molnar · 2020
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