Crafting Adversarial Input Sequences for Recurrent Neural Networks

R. Pascanu et al. , “Malware classification with recurrent networks,” in IEEE ICASSP . IEEE, 2015, pp. 1916–1920

1920

Earlier work this paper cites.

G. E. Hinton, “Learning distributed representations of concepts,” in Proceedings of the eighth annual conference of the cognitive science society , vol. 1. Amherst, MA, 1986, p. 12

1986

Earlier work this paper cites.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Cognitive modeling , vol. 5, 1988

1988

Earlier work this paper cites.

P. J. Werbos, “Generalization of backpropagation with application to a recurrent gas market model,” Neural Networks , vol. 1(4), pp. 339–356, 1988

1988

Earlier work this paper cites.

M. C. Mozer, “A focused back-propagation algorithm for temporal pattern recognition,” Complex systems , vol. 3, no. 4, pp. 349–381, 1989

1989

Earlier work this paper cites.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

C. M. Bishop, “Pattern recognition,” Machine Learning , 2006

2006

Earlier work this paper cites.

M. Barreno, B. Nelson et al. , “Can machine learning be secure?” in Proceedings of the 2006 ACM Symposium on Information, computer and communications security . ACM, 2006, pp. 16–25

2006

Earlier work this paper cites.

D. A. Freedman, Statistical models: theory and practice . Cambridge University Press, 2009

2009

Earlier work this paper cites.

J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, and al., “Theano: a cpu and gpu math expression compiler,” in Proceedings of the Python for scientific computing conference (SciPy) , vol. 4. Austin, TX, 2010, p. 3

2010

Cited alongside, same era.

A. L. Maas et al. , “Learning word vectors for sentiment analysis,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies , Portland, Oregon, USA, 2011, pp. 142–150

2011

Cited alongside, same era.

K. P. Murphy, Machine learning: a probabilistic perspective . MIT press, 2012

2012

Cited alongside, same era.

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

Cited alongside, same era.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in Proceedings of the 2014 International Conference on Learning Representations . Computational and Biological Learning Society, 2014

2014

Later among the works it cites.

I. J. Goodfellow et al. , “Explaining and harnessing adversarial examples,” in Proceedings of the 2015 International Conference on Learning Representations . Computational and Biological Learning Society, 2015

2015

Later among the works it cites.

N. Papernot et al. , “The limitations of deep learning in adversarial settings,” in Proceedings of the 1st IEEE European Symposium on Security and Privacy . IEEE, 2016

2016

Closest in time.

N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, and al., “Practical black-box attacks against deep learning systems using adversarial examples,” arXiv preprint arXiv:1602.02697 , 2016

Original

2016

Closest in time.