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This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions.
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15 (1), 1929–1958
1958
Earlier work this paper cites.
Rumelhart, D. E., Hinton, G. E., Williams, R. J., oct 1986. Learning representations by back-propagating errors. Nature 323 (6088), 533–536
1986
Earlier work this paper cites.
Bishop, C. M., 1995. Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York, NY, USA
1995
Earlier work this paper cites.
Khan, J., Wei, J. S., Ringnér, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., Meltzer, P. S., jun 2001. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7 (6), 673–679
2001
Earlier work this paper cites.
Gevrey, M., Dimopoulos, I., Lek, S., feb 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160 (3), 249–264
2003
Earlier work this paper cites.
Taylor, B. J., 2005. Methods and Procedures for the Verification and Validation of Artificial Neural Networks. Springer-Verlag New York, Inc., Secaucus, NJ, USA
2005
Earlier work this paper cites.
Berkes, P., Wiskott, L., 2006. On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields. Neural Computation 18 (8), 1868–1895
2006
Earlier work this paper cites.
Poulin, B., Eisner, R., Szafron, D., Lu, P., Greiner, R., Wishart, D. S., Fyshe, A., Pearcy, B., Macdonell, C., Anvik, J., 2006. Visual explanation of evidence with additive classifiers. In: Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, July 16-20, 2006, Boston, Massachusetts, USA. pp. 1822–1829
2006
Earlier work this paper cites.
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Müller, K.-R., 2008. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magazine 25 (1), 41–56
2008
Earlier work this paper cites.
Erhan, D., Bengio, Y., Courville, A., Vincent, P., Jun. 2009. Visualizing higher-layer features of a deep network. Tech. Rep. 1341, University of Montreal, also presented at the ICML 2009 Workshop on Learning Feature Hierarchies, Montréal, Canada
2009
Earlier work this paper cites.
Lee, H., Grosse, R. B., Ranganath, R., Ng, A. Y., 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009. pp. 609–616
2009
Earlier work this paper cites.
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., Müller, K.-R., 2010. How to explain individual classification decisions. Journal of Machine Learning Research 11, 1803–1831
2010
Earlier work this paper cites.
Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., Irizarry, R. A., sep 2010. Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics 11 (10), 733–739
2010
Earlier work this paper cites.
Blankertz, B., Lemm, S., Treder, M. S., Haufe, S., Müller, K.-R., 2011. Single-trial analysis and classification of ERP components - A tutorial. NeuroImage 56 (2), 814–825
2011
Earlier work this paper cites.
Hansen, K., Baehrens, D., Schroeter, T., Rupp, M., Müller, K.-R., sep 2011. Visual interpretation of kernel-based prediction models. Molecular Informatics 30 (9), 817–826
2011
Earlier work this paper cites.
Lemm, S., Blankertz, B., Dickhaus, T., Müller, K.-R., 2011. Introduction to machine learning for brain imaging. NeuroImage 56 (2), 387–399
2011
Earlier work this paper cites.
Bengio, Y., 2012. Practical recommendations for gradient-based training of deep architectures. In: Neural Networks: Tricks of the Trade - Second Edition. pp. 437–478
2012
Earlier work this paper cites.
Hinton, G. E., 2012. A practical guide to training restricted Boltzmann machines. In: Neural Networks: Tricks of the Trade - Second Edition. pp. 599–619
2012
Earlier work this paper cites.
Montavon, G., Orr, G., Müller, K.-R., 2012. Neural Networks: Tricks of the Trade, 2nd Edition. Springer Publishing Company, Inc
2012
Earlier work this paper cites.
Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R., Burke, K., jun 2012. Finding density functionals with machine learning. Physical Review Letters 108 (25)
2012
Cited alongside, same era.
Bazen, S., Joutard, X., 2013. The Taylor decomposition: A unified generalization of the Oaxaca method to nonlinear models. Working papers, HAL
2013
Cited alongside, same era.
Landecker, W., Thomure, M. D., Bettencourt, L. M. A., Mitchell, M., Kenyon, G. T., Brumby, S. P., 2013. Interpreting individual classifications of hierarchical networks. In: IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, Singapore, 16-19 April, 2013. pp. 32–38
2013
Cited alongside, same era.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., Dean, J., 2013. Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States. pp. 3111–3119
2016
Later among the works it cites.
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., Samek, W., 2016. Layer-wise relevance propagation for neural networks with local renormalization layers. In: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. pp. 63–71
2016
Later among the works it cites.
Lapuschkin, S., Binder, A., Montavon, G., Müller, K.-R., Samek, W., 2016. Analyzing classifiers: Fisher vectors and deep neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. pp. 2912–2920
2016
Later among the works it cites.
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2013
Cited alongside, same era.
Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.-R., von Lilienfeld, O. A., sep 2013. Machine learning of molecular electronic properties in chemical compound space. New Journal of Physics 15 (9), 095003
2013
Cited alongside, same era.
2013
Cited alongside, same era.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., Bengio, Y., 2014. Generative adversarial nets. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. pp. 2672–2680
2014
Cited alongside, same era.
Haufe, S., Meinecke, F. C., Görgen, K., Dähne, S., Haynes, J.-D., Blankertz, B., Bießmann, F., 2014. On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage 87, 96–110
2014
Cited alongside, same era.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R. B., Guadarrama, S., Darrell, T., 2014. Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, MM’14, Orlando, FL, USA, November 03 - 07, 2014. pp. 675–678
2014
Cited alongside, same era.
Soneson, C., Gerster, S., Delorenzi, M., 06 2014. Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation. PLOS ONE 9 (6), 1–13
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Zeiler, M. D., Fergus, R., 2014. Visualizing and understanding convolutional networks. In: Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I. pp. 818–833
2014
Cited alongside, same era.
Li, J., Chen, X., Hovy, E. H., Jurafsky, D., 2016. Visualizing and understanding neural models in NLP. In: NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12-17, 2016. pp. 681–691
2016
Later among the works it cites.
Lipton, Z. C., 2016. The mythos of model interpretability. CoRR abs/1606.03490
2016
Later among the works it cites.
Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J., 2016a. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. pp. 3387–3395
2016
Later among the works it cites.
Ribeiro, M. T., Singh, S., Guestrin, C., 2016. "why should I trust you?": Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. pp. 1135–1144
2016
Later among the works it cites.
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., 2016. Evaluating the visualization of what a deep neural network has learned. IEEE Transactions on Neural Networks and Learning Systems, 1–14
2016
Later among the works it cites.
Sturm, I., Lapuschkin, S., Samek, W., Müller, K.-R., dec 2016. Interpretable deep neural networks for single-trial EEG classification. Journal of Neuroscience Methods 274, 141–145
2016
Later among the works it cites.
van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K., 2016. Pixel recurrent neural networks. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016. pp. 1747–1756
2016
Later among the works it cites.
2016
Later among the works it cites.
Zhang, J., Lin, Z. L., Brandt, J., Shen, X., Sclaroff, S., 2016. Top-down neural attention by excitation backprop. In: Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV. pp. 543–559
2016
Later among the works it cites.
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2017
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Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R., may 2017. Machine learning of accurate energy-conserving molecular force fields. Science Advances 3 (5), e1603015
2017
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Montavon, G., Lapuschkin, S., Binder, A., Samek, W., Müller, K.-R., 2017. Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognition 65, 211–222
2017
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Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., Tkatchenko, A., jan 2017. Quantum-chemical insights from deep tensor neural networks. Nature Communications 8, 13890
2017
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Vidovic, M. M.-C., Kloft, M., Müller, K.-R., Görnitz, N., 03 2017. Ml2motif–reliable extraction of discriminative sequence motifs from learning machines. PLOS ONE 12 (3), 1–22
2017
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