In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …

Generative adversarial nets (GANs) and variational auto-encoders have significantly
improved our distribution modeling capabilities, showing promise for dataset augmentation …

In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …

Making sense of Wasserstein distances between discrete measures in high-dimensional
settings remains a challenge. Recent work has advocated a two-step approach to improve …

Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-
SW), have been used widely in the recent years due to their fast computation and scalability …

By building upon the recent theory that established the connection between implicit
generative modeling (IGM) and optimal transport, in this study, we propose a novel …

Optimal transport offers an alternative to maximum likelihood for learning generative
autoencoding models. We show that minimizing the $ p $-Wasserstein distance between the …

Class imbalanced datasets are common in real-world applications ranging from credit card
fraud detection to rare disease diagnosis. Recently, deep generative models have proved …

We address the problem of computing a textural loss based on the statistics extracted from
the feature activations of a convolutional neural network optimized for object recognition (eg …

A bstract The lack of evidence for new physics at the Large Hadron Collider so far has
prompted the development of model-independent search techniques. In this study, we …