Regularization
Recurrent Dropout
Introduced by Semeniuta et al. in Recurrent Dropout without Memory LossRecurrent Dropout is a regularization method for recurrent neural networks. Dropout is applied to the updates to LSTM memory cells (or GRU states), i.e. it drops out the input/update gate in LSTM/GRU.
Source: Recurrent Dropout without Memory Loss
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| General Classification | 1 | 25.00% |
| Text Classification | 1 | 25.00% |
| Language Modeling | 1 | 25.00% |
| Language Modelling | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
