Papers › Recurrent Neural Network Regularization
Recurrent Neural Network Regularization
Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation.
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Code
Syntology Ran 2 of 6 code samples harvested from 2 repositories linked to this paper; 4 have no recorded run. Of those that ran: 2 ran · our draft was wrong.
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21 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
6 samples harvested; 2 ran; 0 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 6 of the 6 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | Penn Treebank (Word Level) | Zaremba et al. (2014) - LSTM (large) | Test perplexity | 78.4 | #36 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | Zaremba et al. (2014) - LSTM (large) | Validation perplexity | 82.2 | #36 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | Zaremba et al. (2014) - LSTM (medium) | Test perplexity | 82.7 | #39 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | Zaremba et al. (2014) - LSTM (medium) | Validation perplexity | 86.2 | #39 of 43 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-French | Regularized LSTM | BLEU score | 29.03 | #51 of 57 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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