Recent advances in the sparse neural network literature have made it possible
to prune many large feed forward and convolutional networks with only a small
quantity of data. Yet, these same techniques often falter when applied to the
problem of recovering sparse recurrent networks. These failures are
quantitative: when pruned with recent techniques, RNNs typically obtain worse
performance than they do under a simple random pruning scheme. The failures are
also qualitative: the distribution of active weights in a pruned LSTM or GRU
network tend to be concentrated in specific neurons and gates, and not well
dispersed across the entire architecture. We seek to rectify both the
quantitative and qualitative issues with recurrent network pruning by
introducing a new recurrent pruning objective derived from the spectrum of the
recurrent Jacobian. Our objective is data efficient (requiring only 64 data
points to prune the network), easy to implement, and produces 95% sparse GRUs
that significantly improve on existing baselines. We evaluate on sequential
MNIST, Billion Words, and Wikitext.

这篇论文通过引入一种基于循环雅可比谱的新的循环网络修剪目标，提出了一种高效数据、易于实现的处理方案，能够解决循环网络修剪中的定量和定性问题，并在连续 MNIST、十亿词、Wikitext 等任务中取得了长足进展。