A Gated Recurrent Unit, or GRU, is a type of recurrent neural network. It is similar to an LSTM, but only has two gates - a reset gate and an update gate - and notably lacks an output gate. Fewer parameters means GRUs are generally easier/faster to train than their LSTM counterparts.
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Source: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine TranslationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Time Series Analysis | 49 | 6.07% |
Decoder | 41 | 5.08% |
Speech Synthesis | 39 | 4.83% |
Language Modelling | 25 | 3.10% |
Sentence | 25 | 3.10% |
Time Series Forecasting | 19 | 2.35% |
Sentiment Analysis | 19 | 2.35% |
General Classification | 19 | 2.35% |
Classification | 18 | 2.23% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |