Title: The Statistical Recurrent Unit,
Abstract: Sophisticated gated recurrent neural network architectures like LSTMs and
GRUs have been shown to be highly effective in a myriad of applications. We
develop an un-gated unit, the statistical recurrent unit (SRU), that is able to
learn long term dependencies in data by only keeping moving averages of
statistics. The SRU's architecture is simple, un-gated, and contains a
comparable number of parameters to LSTMs; yet, SRUs perform favorably to more
sophisticated LSTM and GRU alternatives, often outperforming one or both in
various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an
unbiased manner by optimizing respective architectures' hyperparameters in a
Bayesian optimization scheme for both synthetic and real-world tasks.