The remarkable progress in deep learning in recent years is largely driven by improvements in scale, where bigger models are trained on larger datasets for longerschedules.

The remarkable progress in deep learning in recent years is largely driven by improvements in scale, where bigger models are trained on larger datasets for longerschedules.

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.