  Recurrent Neural Networks (RNN) are a type of statistical model designed to
handle sequential data. The model reads a sequence one symbol at a time. Each
symbol is processed based on information collected from the previous symbols.
With existing RNN architectures, each symbol is processed using only
information from the previous processing step. To overcome this limitation, we
propose a new kind of RNN model that computes a recurrent weighted average
(RWA) over every past processing step. Because the RWA can be computed as a
running average, the computational overhead scales like that of any other RNN
architecture. The approach essentially reformulates the attention mechanism
into a stand-alone model. The performance of the RWA model is assessed on the
variable copy problem, the adding problem, classification of artificial
grammar, classification of sequences by length, and classification of the MNIST
images (where the pixels are read sequentially one at a time). On almost every
task, the RWA model is found to outperform a standard LSTM model.
