Expansion property of a graph refers to its strong connectivity as well as
sparseness. It has been reported that deep neural networks can be pruned to a
high degree of sparsity while maintaining their performance. Such pruning is
essential for performing real time sequence learning tasks using recurrent
neural networks in resource constrained platforms. We prune recurrent networks
such as RNNs and LSTMs, maintaining a large spectral gap of the underlying
graphs and ensuring their layerwise expansion properties. We also study the
time unfolded recurrent network graphs in terms of the properties of their
bipartite layers. Experimental results for the benchmark sequence MNIST,
CIFAR-10, and Google speech command data show that expander graph properties
are key to preserving classification accuracy of RNN and LSTM.

神经网络的稀疏性和强连通性是扩展属性，对于在资源受限平台上使用递归神经网络进行实时序列学习任务来说，剪枝是必要的。本研究探讨了保持递归网络的大型谱间隙和层级扩展属性的方法，以及关于它们双分层图特性的时间展开递归网络图研究。通过 MNIST、CIFAR-10 和 Google 语音指令数据的实验结果表明，扩展图特性对于保留递归神经网络和长短期记忆网络的分类准确性至关重要。