Sequence prediction and classification are ubiquitous and challenging
problems in machine learning that can require identifying complex dependencies
between temporally distant inputs. Recurrent Neural Networks (RNNs) have the
ability, in theory, to cope with these temporal dependencies by virtue of the
short-term memory implemented by their recurrent (feedback) connections.
However, in practice they are difficult to train successfully when the
long-term memory is required. This paper introduces a simple, yet powerful
modification to the standard RNN architecture, the Clockwork RNN (CW-RNN), in
which the hidden layer is partitioned into separate modules, each processing
inputs at its own temporal granularity, making computations only at its
prescribed clock rate. Rather than making the standard RNN models more complex,
CW-RNN reduces the number of RNN parameters, improves the performance
significantly in the tasks tested, and speeds up the network evaluation. The
network is demonstrated in preliminary experiments involving two tasks: audio
signal generation and TIMIT spoken word classification, where it outperforms
both RNN and LSTM networks.

本文介绍了简单而强大的时钟 RNN 的修改方法，该方法将隐藏层分成单独的模块，在其自己的时间粒度上处理输入，并且在测试的任务中改善了性能，速度快于标准的 RNN 模型，优于 RNN 和 LSTM 网络，出色地完成了声音信号生成和 TIMIT 口语识别任务。