Before revision:  We then show that UMAP loss can be extended to arbitrary deep learning applications, for example constraining the latent distribution of autoencoders,  and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data.
After revision:  We then show that UMAP loss can be extended to arbitrary deep learning applications, for example constraining the latent distribution of autoencoders, parametrically varying global structure preservation, and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data.