In this paper, we systematically analyse the connecting architectures of recurrent neural networks (RNNs). Our main contribution is twofold: first, we present a rigorous graph-theoretic framework describing the connecting architectures of RNNs in general. Second, we propose three architecture complexity measures of RNNs: (a) the recurrent depth, which captures the RNN's over-time nonlinear complexity, (b) the feedforward depth, which captures the local input-output nonlinearity (similar to the "depth" in feedforward neural networks (FNNs)), and (c) the recurrent skip coefficient which captures how rapidly the information propagates over time. Our experimental results show that RNNs might benefit from larger recurrent depth and feedforward depth. We further demonstrate that increasing recurrent skip coefficient offers performance boosts on long term dependency problems, as we improve the state-of-the-art for sequential MNIST dataset.

该论文系统分析了循环神经网络连接体系结构，并提出了三种体系结构复杂度量度，包括循环深度、前馈深度和循环跳跃系数，并通过实验结果发现增加循环深度和前馈深度可以改善RNN的表现，在长期依赖问题上提高循环跳跃系数可以提升性能。

循环神经网络的结构复杂度度量