Recurrent Neural Networks
Gated Recurrent Unit
Introduced by Cho et al. in Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine TranslationA 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.
Image Source: here
Source: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Time Series Analysis | 53 | 7.21% |
| Speech Synthesis | 39 | 5.31% |
| Language Modelling | 27 | 3.67% |
| General Classification | 27 | 3.67% |
| Sentiment Analysis | 24 | 3.27% |
| Classification | 18 | 2.45% |
| Text-To-Speech Synthesis | 15 | 2.04% |
| Time Series Forecasting | 15 | 2.04% |
| Speech Recognition | 14 | 1.90% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
