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We develop Neuron Shapley as a new framework to quantify the contribution of individual neurons to the prediction and performance of a deep network.
A value for n-person games
L. S. Shapley · 1953
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Values of large games. 6: Evaluating the electoral college exactly
I. Mann and L. S. Shapley · 1962
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The Shapley value: essays in honor of Lloyd S. Shapley
L. S. Shapley, A. E. Roth, et al · 1988
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Approximations of pseudo-boolean functions; applications to game theory
P. L. Hammer and R. Holzman · 1992
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Empirical bernstein stopping
V. Mnih, C. Szepesvári, and J.-Y. Audibert · 2008
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Polynomial calculation of the shapley value based on sampling
J. Castro, D. Gómez, and J. Tejada · 2009
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Empirical bernstein bounds and sample variance penalization
A. Maurer and M. Pontil · 2009
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An efficient explanation of individual classifications using game theory
I. Kononenko et al · 2010
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Bounding the estimation error of sampling-based shapley value approximation
S. Maleki, L. Tran-Thanh, G. Hines, T. Rahwan, and A. Rogers · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
A. Binder, G. Montavon, S. Lapuschkin, K.-R. Müller, and W. Samek · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
A. Datta, S. Sen, and Y. Zick · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
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Non-stochastic best arm identification and hyperparameter optimization
K. Jamieson and A. Talwalkar · 2016
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Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 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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B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, and R. Sayres · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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L-shapley and c-shapley: Efficient model interpretation for structured data
J. Chen, L. Song, M. J. Wainwright, and M. I. Jordan · 2018
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K. Dhamdhere, M. Sundararajan, and Q. Yan · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
J. Frankle and M. Carbin · 2018
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Influence-directed explanations for deep convolutional networks
K. Leino, S. Sen, A. Datta, M. Fredrikson, and L. Li · 2018
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Hyperband: A novel bandit-based approach to hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Cited alongside, same era.
Explaining nonlinear classification decisions with deep taylor decomposition
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller · 2017
Cited alongside, same era.
Feature visualization
C. Olah, A. Mordvintsev, and L. Schubert · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
Cited alongside, same era.
Large-scale celebfaces attributes (celeba) dataset
Z. Liu, P. Luo, X. Wang, and X. Tang · 2018
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Analysing neural network topologies: a game theoretic approach
J. Stier, G. Gianini, M. Granitzer, and K. Ziegler · 2018
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Interpretation of neural networks is fragile
A. Ghorbani, A. Abid, and J. Zou · 2019
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Towards automatic concept-based explanations
A. Ghorbani, J. Wexler, J. Y. Zou, and B. Kim · 2019
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Data shapley: Equitable valuation of data for machine learning
A. Ghorbani and J. Zou · 2019
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Towards efficient data valuation based on the shapley value
R. Jia, D. Dao, B. Wang, F. A. Hubis, N. Hynes, N. M. Gurel, B. Li, C. Zhang, D. Song, and C. Spanos · 2019
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Multiaccuracy: Black-box post-processing for fairness in classification
M. P. Kim, A. Ghorbani, and J. Zou · 2019
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Explaining black box decisions by shapley cohort refinement
M. Mase, A. B. Owen, and B. Seiler · 2019
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Adaptive monte carlo multiple testing via multi-armed bandits
M. J. Zhang, J. Zou, and D. Tse · 2019
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From local explanations to global understanding with explainable ai for trees
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee · 2020
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