关键词: 循环神经网络, 长短时记忆网络, 低秩重构压缩, 基于时间误差的低秩重构压缩

Abstract: Recurrent neural networks are widely used in various sequence data processing tasks, such as machine translation, speech recognition, image annotation and so on. The language model based on recurrent neural networks usually contains a large number of parameters, which limits the use of the model on mobile devices or embedded devices to some extent. Aiming at this problem, a low rank reconstruction compression method based on time-error is proposed, which adds the time-error reconstruction function on the basis of low rank reconstruction compression, and the input activation mechanism of long short-term memory network is adopted. Numerical experiments on multiple data sets show that the proposed method has a better effect on compression.

Key words: recurrent neural networks, long short-term memory, low rank reconstruction compression, low rank reconstruction compression based on time-error