Development of Probabilistic Cardinal Models

Cardinal models solve problems of the form Y=X_1+X_2+...+X_n, where we have discrete distributions on each random variable. The objective was to improve the usability and performance of Evergreen, an engine for solving cardinal models and to create cardinal models using them

Adversarial network training using higher-order moments in a modified Wasserstein distance

Generative-adversarial networks (GANs) have been used to produce data closely resembling example data in a compressed, latent space that is close to sufficient for reconstruction in the original vector space. The Wasserstein metric has been used as an alternative to binary cross-entropy, producing more numerically stable GANs with greater mode covering behavior. Here, a generalization of the Wasserstein distance, using higher-order moments than the mean, is derived. Training a GAN with this higher-order Wasserstein metric is demonstrated to exhibit superior performance, even when adjusted for slightly higher computational cost. This is illustrated generating synthetic antibody sequences