We present an optimal transport framework for learning topics from textual data. While the celebrated Latent Dirichlet allocation (LDA) topic model and its variants have been applied to many disciplines, they mainly focus on wordoccurrences and neglect to incorporate semantic regularities in language.

Internal learning for single-image generation is a framework where a generatoristrained toproduce novelimages based on asingle image. Since these modelsare trained on asingle image, theyare limited in their scale and application.

Inthis paper,werely onabroader viewofpropercompositelosses and arecent construct from information geometry,sourcefunctions, whose fitting alleviates constraints faced by canonical links.

We present a case study on malware detection--a binary classification problem on byte sequences where classifier evasion is a well-established threat model.

In order to explore the impact of pandemics on people's lives, itiscrucial to understand the public'sconcerns and attitudes towards pandemicrelated entities (e.g., drugs, vaccines) onsocial media.