Predictive variational autoencoder for learning robust representations
  of time-series data
Julia Huiming Wang Dexter Tsin Tatiana Engel
Variational autoencoders (VAEs) have been used extensively to discover
low-dimensional latent factors governing neural activity and animal behavior.
However, without careful model selection, the uncovered latent factors may
reflect noise in the data rather than true underlying features, rendering such
representations unsuitable for scientific interpretation. Existing solutions to
this problem involve introducing additional measured variables or data
augmentations specific to a particular data type. We propose a VAE architecture
that predicts the next point in time and show that it mitigates the learning of
spurious features. In addition, we introduce a model selection metric based on
smoothness over time in the latent space. We show that together these two
constraints on VAEs to be smooth over time produce robust latent
representations and faithfully recover latent factors on synthetic datasets.