Lightlike Neuromanifolds, Occam's Razor and Deep Learning

05/27/2019
by   Ke Sun, et al.
3

Why do deep neural networks generalize with a very high dimensional parameter space? We took an information theoretic approach. We find that the dimensionality of the parameter space can be studied by singular semi-Riemannian geometry and is upper-bounded by the sample size. We adapt Fisher information to this singular neuromanifold. We use random matrix theory to derive a minimum description length of a deep learning model, where the spectrum of the Fisher information matrix plays a key role to improve generalisation.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset