Methods › General › Regularization › Recurrent Dropout
Recurrent Dropout
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Recurrent 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.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention 1 Oct 2018 · 1 repository
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Revisiting Activation Regularization for Language RNNs 3 Aug 2017 · 0 repositories · arXiv:1708.01009
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Recurrent Dropout without Memory Loss 16 Mar 2016 · 2 repositories · arXiv:1603.05118
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| General Classification | 1 |
| L2 Regularization | 1 |
| Language Modeling | 1 |
| Language Modelling | 1 |
| Text Classification | 1 |
| Word Embeddings | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections