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In this paper we introduce a novel, unified, open-source model interpretability library for PyTorch [12].
Hedonic housing prices and the demand for clean air
David Harrison and Daniel L Rubinfeld · 1978
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Visualizing and understanding convolutional networks
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2015
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 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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On the robustness of interpretability methods
David Alvarez-Melis and Tommi S. Jaakkola · 2018
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco B Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
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Learning explainable models using attribution priors, 2019
Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2019
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Alibi: Algorithms for monitoring and explaining machine learning models, 2019
Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, and Alexandru Coca · 2019
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Interpretml: A unified framework for machine learning interpretability, 2019
Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana · 2019
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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 Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Benchmarking attribution methods with relative feature importance, 2019
Mengjiao Yang and Been Kim · 2019
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Explaining explanations: An overview of interpretability of machine learning
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal · 2018
Cited alongside, same era.
How important is a neuron?
Kedar Dhamdhere, Mukund Sundararajan, and Qiqi Yan · 2019
Cited alongside, same era.
On the (in)fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, and Pradeep D. Ravikumar · 2019
Later among the works it cites.