  We propose a new method for modeling the distribution function of high
dimensional extreme value distributions. The Pickands dependence function
models the relationship between the covariates in the tails, and we learn this
function using a neural network that is designed to satisfy its required
properties. Moreover, we present new methods for recovering the spectral
representation of extreme distributions and propose a generative model for
sampling from extreme copulas. Numerical examples are provided demonstrating
the efficacy and promise of our proposed methods.
