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Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions.
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Fully convolutional networks for semantic segmentation
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Imagenet large scale visual recognition challenge
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Understanding black-box predictions via influence functions
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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https://www.blog.google/technology/ai/ai-principles/
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Falling rule lists
F. Wang and C. Rudin · 2015
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European union regulations on algorithmic decision-making and a “right to explanation”
B. Goodman and S. Flaxman · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
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Model-agnostic interpretability of machine learning
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Network dissection: Quantifying interpretability of deep visual representations
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba · 2017
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J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2018
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Knockoffs for the mass: new feature importance statistics with false discovery guarantees
J. R. Gimenez, A. Ghorbani, and J. Zou · 2018
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Sanity checks for saliency maps
M. M. I. G. M. H. B. K. Julius Adebayo, Justin Gilmer · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, and R. Sayres · 2018
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Manipulating and measuring model interpretability
F. Poursabzi-Sangdeh, D. G. Goldstein, J. M. Hofman, J. W. Vaughan, and H. Wallach · 2018
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Superpixel hierarchy
X. Wei, Q. Yang, Y. Gong, N. Ahuja, and M.-H. Yang · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
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Interpretable basis decomposition for visual explanation
B. Zhou, Y. Sun, D. Bau, and A. Torralba · 2018
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
W. Brendel and M. Bethge · 2019
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