In this paper, we systematically analyze 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. We rigorously prove each
measure's existence and computability. 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.

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。