  Inspired by the success of deep learning techniques in the physical and
chemical sciences, we apply a modification of an autoencoder type deep neural
network to the task of dimension reduction of molecular dynamics data. We can
show that our time-lagged autoencoder reliably finds low-dimensional embeddings
for high-dimensional feature spaces which capture the slow dynamics of the
underlying stochastic processes - beyond the capabilities of linear dimension
reduction techniques.
