of the 34th International Conference on Machine Learning*]{}, pages 2564–2573, 2017.

M. Raghu, J. Gilmer, J. Yosinski, and J. Sohl-Dickstein. : Singular vector canonical correlation analysis for deep learning dynamics and interpretability. In [*Advances in Neural Information Processing Systems 30*]{}, pages 6076–6085, 2017.

M. T. Ribeiro, S. Singh, and C. Guestrin. “[W]{}hy 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*]{}, pages 1135–1144, 2016.

D. Rumelhart, G. Hinton, and R. Williams. Learning representations by back-propagating errors. , 323:533–536, 1986.

A. Shrikumar, P. Greenside, and A. Kundaje. Learning important features through propagating activation differences. In [*Proceedings of the 34th International Conference on Machine Learning*]{}, pages 3145–3153, 2017.

K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. In [*ICLR 2014 Workshop*]{}, 2014.

D. Smilkov, N. Thorat, B. Kim, F. Vi[é]{}gas, and M. Wattenberg. Smoothgrad: removing noise by adding noise. arXiv:1706.03825, 2017.

J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller. Striving for simplicity: The all convolutional net. In [*ICLR 2015 Workshop*]{}, 2015.

M. Sundararajan, A. Taly, and Q. Yan. Axiomatic attribution for deep networks. In [*Proceedings 