Residual Recurrent Neural Networks for Learning Sequential Representations
Recurrent neural networks (RNN) are efficient in modeling sequences for generation and classification, but their training is obstructed by the vanishing and exploding gradient issues. In this paper, we reformulate the RNN unit to learn the residual functions with reference to the hidden state instea...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-03-01
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| Series: | Information |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2078-2489/9/3/56 |