Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks
2017/01/20 by Rahul Dey, Fathi M. Salem, Dey, Rahul +1 · 66 citations
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #cs.NE #stat.ML
paper · pdf · doi:10.48550/arxiv.1701.05923
5 pages, 8 Figures, 4 Tables
arxiv created 2017/01/20 · arxiv updated 2017/01/24
Abstract
The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense.
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