Fetching the paper…
Reading the bibliography…
Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application.
B. Poulin, R. Eisner, D. Szafron, P. Lu, R. Greiner, D. S. Wishart, A. Fyshe, B. Pearcy, C. Macdonell, and J. Anvik, “Visual explanation of evidence with additive classifiers,” in Proc. of the 21st National Conference on Artificial Intelligence , 2006, pp. 1822–1829
2006
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al. , “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Processing Magazine , vol. 29, no. 6, pp. 82–97, 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
W. Landecker, M. D. Thomure, L. M. A. Bettencourt, M. Mitchell, G. T. Kenyon, and S. P. Brumby, “Interpreting individual classifications of hierarchical networks,” in IEEE Symposium on Computational Intelligence and Data Mining , 2013, pp. 32–38
2013
Earlier work this paper cites.
Y. Kim, “Convolutional neural networks for sentence classification,” CoRR , vol. abs/1408.5882, 2014
2014
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in ECCV , 2014, pp. 818–833
2014
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proc. of the 22nd ACM Int. Conf. on Multimedia , 2014, pp. 675–678
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,” PLOS ONE , vol. 10, no. 7, p. e0130140, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
J. Zhang, Z. Lin, J. Brandt, X. Shen, and S. Sclaroff, “Top-down neural attention by excitation backprop,” in European Conference on Computer Vision . Springer, 2016, pp. 543–559
2016
Cited alongside, same era.
2016
Closest in time.
S. Lapuschkin, A. Binder, G. Montavon, K.-R. Müller, and W. Samek, “Analyzing classifiers: Fisher vectors and deep neural networks,” in Proc. of IEEE CVPR , 2016, pp. 2912–2920
2016
Closest in time.
L. Arras, F. Horn, G. Montavon, K.-R. Müller, and W. Samek, “Explaining predictions of non-linear classifiers in nlp,” in Proc. of the 1st ACL Workshop on Representation Learning for NLP , 2016, pp. 1–7
2016
Closest in time.
F. Arbabzadeh, G. Montavon, K.-R. Müller, and W. Samek, “Identifying individual facial expressions by deconstructing a neural network,” in GCPR 2016 , ser. LNCS. Springer, 2016, vol. 9796, pp. 344–354
2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
2016
Cited alongside, same era.
G. Kasneci and T. Gottron, “Licon: A linear weighting scheme for the contribution ofinput variables in deep artificial neural networks,” in Proc. of the 25th ACM Int. Conf. on Information and Knowledge Management . ACM, 2016, pp. 45–54
2016
Cited alongside, same era.
I. Sturm, S. Lapuschkin, W. Samek, and K.-R. Müller, “Interpretable deep neural networks for single-trial eeg classification,” Journal of Neuroscience Methods , vol. 274, pp. 141–145, 2016
2016
Closest in time.
W. Samek, A. Binder, G. Montavon, S. Bach, and K.-R. Müller, “Evaluating the visualization of what a deep neural network has learned,” IEEE TNNLS , 2016
2016
Closest in time.
A. Binder, W. Samek, G. Montavon, S. Bach, and K.-R. Müller, “Analyzing and validating neural networks predictions,” in Proceedings of the Workshop on Visualization for Deep Learning at International Conference on Machine Learning (ICML) , 2016
2016
Closest in time.