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 | 52 | 6.67% |
| Speech Synthesis | 40 | 5.13% |
| Language Modelling | 25 | 3.21% |
| Sentence | 25 | 3.21% |
| Sentiment Analysis | 20 | 2.56% |
| Time Series Forecasting | 19 | 2.44% |
| General Classification | 19 | 2.44% |
| Classification | 17 | 2.18% |
| Text-To-Speech Synthesis | 14 | 1.79% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
