Variational autoencoders (VAEs), as well as other generative models, have been shown to
be efficient and accurate for capturing the latent structure of vast amounts of complex high …

Neural computations underlying cognition and behavior rely on the coordination of neural
activity across multiple brain areas. Understanding how brain areas interact to process …

The increasing availability of large-scale single-cell atlases has enabled the detailed
description of cell states. In parallel, advances in deep learning allow rapid analysis of newly …

Motivation Single-cell RNA-seq makes possible the investigation of variability in gene
expression among cells, and dependence of variation on cell type. Statistical inference …

We develop the sparse VAE for unsupervised representation learning on high-dimensional
data. The sparse VAE learns a set of latent factors (representations) which summarize the …

Disentangled generative models map a latent code vector to a target space, while enforcing
that a subset of the learned latent codes are interpretable and associated with distinct …

Cross-modal biometric matching (CMBM) aims to determine the corresponding voice from a
face, or identify the corresponding face from a voice. Recently, many CMBM methods have …

There have been many recent advances in representation learning; however, unsupervised
representation learning can still struggle with model identification issues related to rotations …

Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that
aims at separating the common factors of variation between a background dataset (BG)(ie …

Medical pathology images are visually evaluated by experts for disease diagnosis, but the
connection between image features and the state of the cells in an image is typically …