. . . . . . . Background . . . . . . . . . . . . Information geometry of dropout . . . . . . . . . . . . . . Dropout submanifolds and Regularization . . . . . . Numerical experiments . . . . Conclusion and discussion References References I Shun-ichi Amari. Information geometry of the em and em algorithms for neural networks. Neural networks, 8(9):1379–1408, 1995. Shun-ichi Amari. Information geometry and its applications, volume 194. Springer, 2016. Shun-ichi Amari and Hiroshi Nagaoka. Methods of information geometry, volume 191. American Mathematical Soc., 2000. Nihat Ay, Jürgen Jost, Hông Vân Lê, and Lorenz Schwachhöfer. Information geometry, volume 64. Springer, 2017. Olivier Catoni. A pac-bayesian approach to adaptive classification. preprint, 840, 2003. Li Deng. The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6):141–142, 2012. Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. Knowledge distillation: A survey. International Journal of Computer Vision, 129(6):1789–1819, 2021. 28/29