COMPRESSED RECURRENT NEURAL NETWORK MODELS

摘要

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing long-short term memory layers with compressed gating functions. One of the systems includes a first long short-term memory (LSTM) layer, wherein the first LSTM layer is configured to, for each of the plurality of time steps, generate a new layer state and a new layer output by applying a plurality of gates to a current layer input, a current layer state, and a current layer output, each of the plurality of gates being configured to, for each of the plurality of time steps, generate a respective intermediate gate output vector by multiplying a gate input vector and a gate parameter matrix. The gate parameter matrix for at least one of the plurality of gates is a structured matrix or is defined by a compressed parameter matrix and a projection matrix.

主权项

1. A system comprising:
a recurrent neural network implemented by one or more computers, wherein the recurrent neural network is configured to receive a respective neural network input at each of a plurality of time steps and to generate a respective neural network output at each of the plurality of time steps, and wherein the recurrent neural network comprises:
a first long short-term memory (LSTM) layer, wherein the first LSTM layer is configured to, for each of the plurality of time steps, generate a new layer state and a new layer output by applying a plurality of gates to a current layer input, a current layer state, and a current layer output, each of the plurality of gates being configured to, for each of the plurality of time steps, generate a respective intermediate gate output vector by multiplying a gate input vector and a gate parameter matrix, and wherein the gate parameter matrix for at least one of the plurality of gates is a Toeplitz-like structured matrix.