The paper systematically analyzes the phenomenon of varying sparsity ratios across views in multi-view learning, and proposed a targeted data-driven network architecture based on Sparse Autoencoder with Adaptive Constraints.

We introduce a graph AutoEncoder that can encode (and decode) graphs of different sizes into the same embedding space.

We developed a functional autoencoder framework for cluster analysis of multi-dimensional functional data, data that are random realizations of vector-valued random functions.

We designed and tested an autoencoder model for stitching together multi-area multi-animal neuronal recording datasets and inpainting neural dynamics from unobserved brain areas in each experiment.