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• W2951844372 abstract "This paper introduces two recurrent neural network structures called Simple Gated Unit (SGU) and Deep Simple Gated Unit (DSGU), which are general structures for learning long term dependencies. Compared to traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM, which require more than one gates to control information flow in the network, SGU and DSGU only use one multiplicative gate to control the flow of information. We show that this difference can accelerate the learning speed in tasks that require long dependency information. We also show that DSGU is more numerically stable than SGU. In addition, we also propose a standard way of representing inner structure of RNN called RNN Conventional Graph (RCG), which helps analyzing the relationship between input units and hidden units of RNN." @default.
• W2951844372 created "2019-06-27" @default.
• W2951844372 creator A5001270670 @default.
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• W2951844372 date "2016-04-11" @default.
• W2951844372 modified "2023-09-27" @default.
• W2951844372 title "Deep Gate Recurrent Neural Network" @default.
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• W2951844372 hasPublicationYear "2016" @default.
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