  This paper proposes a method for machine learning from unlabeled data in the
form of a time-series. The mapping that is learned is shown to extract slowly
evolving information that would be useful for control applications, while
efficiently filtering out unwanted, higher-frequency noise.
  The method consists of training a feedforward artificial neural network with
backpropagation using two opposing objectives.
  The first of these is to minimize the squared changes in activations between
time steps of each unit in the network. This "temporal smoothing" has the
effect of correlating inputs that occur close in time with outputs that are
close in the L2-norm.
  The second objective is to maximize the log determinant of the covariance
matrix of activations in each layer of the network. This objective ensures that
information from each layer is passed through to the next. This second
objective acts as a balance to the first, which on its own would result in a
network with all input weights equal to zero.
