Title: Unreasonable Effectivness of Deep Learning,
Abstract: We show how well known rules of back propagation arise from a weighted
combination of finite automata. By redefining a finite automata as a predictor
we combine the set of all $k$-state finite automata using a weighted majority
algorithm. This aggregated prediction algorithm can be simplified using
symmetry, and we prove the equivalence of an algorithm that does this. We
demonstrate that this algorithm is equivalent to a form of a back propagation
acting in a completely connected $k$-node neural network. Thus the use of the
weighted majority algorithm allows a bound on the general performance of deep
learning approaches to prediction via known results from online statistics. The
presented framework opens more detailed questions about network topology; it is
a bridge to the well studied techniques of semigroup theory and applying these
techniques to answer what specific network topologies are capable of
predicting. This informs both the design of artificial networks and the
exploration of neuroscience models.