Fetching the paper…
Reading the bibliography…
We formalise the widespread idea of interpreting neural network decisions as an explicit optimisation problem in a rate-distortion framework.
A value for n-person games
L. S. Shapley · 1953
Earlier work this paper cites.
Computational complexity of probabilistic turing machines
J. Gill · 1977
Earlier work this paper cites.
Updating formulae and a pairwise algorithm for computing sample variances
T. F. Chan, G. H. Golub, and R. J. LeVeque · 1982
Earlier work this paper cites.
Tractable inference for complex stochastic processes
X. Boyen and D. Koller · 1998
Earlier work this paper cites.
Bucket elimination: A unifying framework for probabilistic inference
R. Dechter · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
The computational complexity of probabilistic planning
M. L. Littman, J. Goldsmith, and M. Mundhenk · 1998
Earlier work this paper cites.
A Family of Algorithms for Approximate Bayesian Inference
T. P. Minka · 2001
Earlier work this paper cites.
Map complexity results and approximation methods
J. D. Park · 2002
Earlier work this paper cites.
Numerical Optimization
J. Nocedal and S. J. Wright · 2006
Cited alongside, same era.
An analysis of single-layer networks in unsupervised feature learning
A. Coates, A. Ng, and H. Lee · 2011
Cited alongside, same era.
Scalability of Semantic Analysis in Natural Language Processing
R. Řehůřek · 2011
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Cited alongside, same era.
An evaluation of the integral of the product of the error function and the normal probability density with application to the bivariate normal integral
H. Fayed and A. Atiya · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
"why should I trust you?": Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Later among the works it cites.
A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
Later among the works it cites.
Evaluating the visualization of what a deep neural network has learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.-R. Müller · 2017
Later among the works it cites.
Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
Later among the works it cites.
Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. B. Viégas, and M. Wattenberg · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
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
Cited alongside, same era.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2015
Cited alongside, same era.
M. Alber, S. Lapuschki, P. Seegerer, M. Hägele, K. T. Schütt, G. Montavon, W. Samek, K. Müller, S. Dähne, and P. Kindermans · 2018
Later among the works it cites.
Lightweight probabilistic deep networks
J. Gast and S. Roth · 2018
Later among the works it cites.
Methods for interpreting and understanding deep neural networks
G. Montavon, W. Samek, and K.-R. Müller · 2018
Later among the works it cites.
The computational complexity of understanding network decisions
S. Wäldchen, J. Macdonald, S. Hauch, and G. Kutyniok · 2019
Closest in time.