Fetching the paper…
Reading the bibliography…
We introduce instancewise feature selection as a methodology for model interpretation.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
An introduction to variable and feature selection
Guyon, I. and Elisseeff, A · 2003
Earlier work this paper cites.
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
Peng, H., Long, F., and Ding, C · 2005
Earlier work this paper cites.
How to explain individual classification decisions
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and MÞller, K.-R · 2010
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
Earlier work this paper cites.
Elements of information theory
Cover, T. M. and Thomas, J. A · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
Earlier work this paper cites.
Multiple object recognition with visual attention
Ba, J., Mnih, V., and Kavukcuoglu, K · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2014
Cited alongside, same era.
Convolutional neural networks for sentence classification
Kim, Y · 2014
Cited alongside, same era.
A* sampling
Maddison, C. J., Tarlow, D., and Minka, T · 2014
Cited alongside, same era.
Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Cited alongside, same era.
Investigating the influence of noise and distractors on the interpretation of neural networks
Kindermans, P.-J., Schütt, K., Müller, K.-R., and Dähne, S · 2016
Later among the works it cites.
The mythos of model interpretability
Lipton, Z. C · 2016
Later among the works it cites.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
Later among the works it cites.
Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Later among the works it cites.
Kernel feature selection via conditional covariance minimization
Chen, J., Stern, M., Wainwright, M. J., and Jordan, M. I · 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…
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
Cited alongside, same era.
A hierarchical neural autoencoder for paragraphs and documents
Li, J., Luong, M.-T., and Jurafsky, D · 2015
Cited alongside, same era.
Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y · 2015
Cited alongside, same era.
Zhang, Y. and Wallace, B · 2015
Cited alongside, same era.
Variational information maximization for feature selection
Gao, S., Ver Steeg, G., and Galstyan, A · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
Later among the works it cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Later among the works it cites.
Online and linear-time attention by enforcing monotonic alignments
Raffel, C., Luong, T., Liu, P. J., Weiss, R. J., and Eck, D · 2017
Later among the works it cites.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Later among the works it cites.
Greedy attack and gumbel attack: Generating adversarial examples for discrete data
Yang, P., Chen, J., Hsieh, C.-J., Wang, J.-L., and Jordan, M. I · 2018
Closest in time.