The Statistical Recurrent Unit
This offers a simpler alternative to gated RNNs for practitioners in sequence modeling, though it appears incremental as it builds on existing architectures.
The paper tackles the problem of learning long-term dependencies in data by proposing the Statistical Recurrent Unit (SRU), an un-gated recurrent neural network that uses moving averages of statistics, and shows it often outperforms LSTMs and GRUs in various tasks.
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.