Title: A Separation Principle for Control in the Age of Deep Learning,
Abstract: We review the problem of defining and inferring a "state" for a control
system based on complex, high-dimensional, highly uncertain measurement streams
such as videos. Such a state, or representation, should contain all and only
the information needed for control, and discount nuisance variability in the
data. It should also have finite complexity, ideally modulated depending on
available resources. This representation is what we want to store in memory in
lieu of the data, as it "separates" the control task from the measurement
process. For the trivial case with no dynamics, a representation can be
inferred by minimizing the Information Bottleneck Lagrangian in a function
class realized by deep neural networks. The resulting representation has much
higher dimension than the data, already in the millions, but it is smaller in
the sense of information content, retaining only what is needed for the task.
This process also yields representations that are invariant to nuisance factors
and having maximally independent components. We extend these ideas to the
dynamic case, where the representation is the posterior density of the task
variable given the measurements up to the current time, which is in general
much simpler than the prediction density maintained by the classical Bayesian
filter. Again this can be finitely-parametrized using a deep neural network,
and already some applications are beginning to emerge. No explicit assumption
of Markovianity is needed; instead, complexity trades off approximation of an
optimal representation, including the degree of Markovianity.