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Shapley values have become one of the most popular feature attribution explanation methods.
Learning explainable models using attribution priors
Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 1906
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A value for n-person games
Lloyd S Shapley · 1953
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Tractable inference for complex stochastic processes
Xavier Boyen and Daphne Koller · 1998
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Shapley Value as Principled Metric for Structured Network Pruning
Marco Ancona, Cengiz Öztireli, and Markus Gross · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky · 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 and Corinna Cortes · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
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An efficient explanation of individual classifications using game theory
Erik Strumbelj and Igor Kononenko · 2010
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Intelligible models for classification and regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
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Accurate intelligible models with pairwise interactions
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker · 2013
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Deep convolutional network cascade for facial point detection
Y. Sun, X. Wang, and X. Tang · 2013
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Multi-stage contextual deep learning for pedestrian detection
X. Zeng, W. Ouyang, and X. Wang · 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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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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Conditional computation in neural networks using a decision-theoretic approach
P. Bacon · 2015
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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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Natural neural networks
Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, and koray kavukcuoglu · 2015
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J. Dally · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Empirical Evaluation of Rectified Activations in Convolutional Network
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li · 2015
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Conditional computation in neural networks for faster models
Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Decision forests, convolutional networks and the models in-between, 2016
Yani Ioannou, Duncan Robertson, Darko Zikic, Peter Kontschieder, Jamie Shotton, Matthew Brown, and Antonio Criminisi · 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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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
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Adaptive neural networks for efficient inference
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Quantitative trait loci identification for brain endophenotypes via new additive model with random networks
Xiaoqian Wang, Hong Chen, Jingwen Yan, Kwangsik Nho, Shannon L Risacher, Andrew J Saykin, Li Shen, Heng Huang, and for the ADNI · 2018
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Explaining deep neural networks with a polynomial time algorithm for shapley value approximation
Marco Ancona, Cengiz Öztireli, and Markus Gross · 2019
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L-shapley and c-shapley: Efficient model interpretation for structured data
Jianbo Chen, Le Song, Martin J. Wainwright, and Michael I. Jordan · 2019
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Incorporating priors with feature attribution on text classification
Frederick Liu and Besim Avci · 2019
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Tolga Bolukbasi, Joseph Wang, Ofer Dekel, and Venkatesh Saligrama · 2017
Cited alongside, same era.
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee · 2017
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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Hierarchical interpretations for neural network predictions
Chandan Singh, W. James Murdoch, and Bin Yu · 2019
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Full-gradient representation for neural network visualization
Suraj Srinivas and François Fleuret · 2019
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On the (in)fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar · 2019
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Neural additive models: Interpretable machine learning with neural nets
Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, and Geoffrey E Hinton · 2020
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Neuron shapley: Discovering the responsible neurons
Amirata Ghorbani and James Y Zou · 2020
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From local explanations to global understanding with explainable AI for trees
Scott M. Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee · 2020
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Regularizing black-box models for improved interpretability
Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric Xing, and Ameet Talwalkar · 2020
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Interpretations are useful: Penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, William Murdoch, and Bin Yu · 2020
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The many shapley values for model explanation
Mukund Sundararajan and Amir Najmi · 2020
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Fine-tuning darts for image classification, 2020
Muhammad Suhaib Tanveer, Muhammad Umar Karim Khan, and Chong-Min Kyung · 2020
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Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics
Alex M. Tseng, Avanti Shrikumar, and Anshul Kundaje · 2020
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A branching and merging convolutional network with homogeneous filter capsules, 2021
Adam Byerly, Tatiana Kalganova, and Ian Dear · 2021
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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An Empirical Study on the Relation Between Network Interpretability and Adversarial Robustness
Adam Noack, Isaac Ahern, Dejing Dou, and Boyang Li · 2021
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On sampling from a finite set of independent random variables
Bengt von Bahr · 2064
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