Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data. Much of this progress has been achieved through devising recurrent units and architectures with the flexibility to capture complex statistics in the data, such as long range dependency or localized attention phenomena. However, while many sequential data (such as video, speech or language) can have highly variable information flow, most recurrent models still consume input features at a constant rate and perform a constant number of computations per time step, which can be detrimental to both speed and model capacity. In this paper, we explore a modification to existing recurrent units which allows them to learn to vary the amount of computation they perform at each step, without prior knowledge of the sequence's time structure. We show experimentally that not only is our model more computationally efficient, it also leads to better performance overall on our evaluation tasks.

本研究提出了一种改进的循环单元，使其能够在学习时不需要事先了解序列的时间结构就能够灵活地调整每个时间步骤的计算量，从而提高模型的容量和性能，实验证明该方法能够在评估任务中取得更好的性能表现并减少计算量。

递归神经网络中的变量计算