Invention Application
- Patent Title: COMPRESSED RECURRENT NEURAL NETWORK MODELS
- Patent Title (中): 压缩性循环神经网络模型
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Application No.: US15172457Application Date: 2016-06-03
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Publication No.: US20170076196A1Publication Date: 2017-03-16
- Inventor: Tara N. Sainath , Vikas Sindhwani
- Applicant: Google Inc.
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/08

Abstract:
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.
Public/Granted literature
- US10515307B2 Compressed recurrent neural network models Public/Granted day:2019-12-24
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