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 explore UMAP as a regularization, constraining the latent distribution of autoencoders, and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data.