Papers › A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Yarin Gal, Zoubin Ghahramani
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout. This grounding of dropout in approximate Bayesian inference suggests an extension of the theoretical results, offering insights into the use of dropout with RNN models. We apply this new variational inference based dropout technique in LSTM and GRU models, assessing it on language modelling and sentiment analysis tasks. The new approach outperforms existing techniques, and to the best of our knowledge improves on the single model state-of-the-art in language modelling with the Penn Treebank (73.4 test perplexity). This extends our arsenal of variational tools in deep learning.
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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) | Gal & Ghahramani (2016) - Variational LSTM (large) | Test perplexity | 75.2 | #35 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | Gal & Ghahramani (2016) - Variational LSTM (large) | Validation perplexity | 77.9 | #35 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | Gal & Ghahramani (2016) - Variational LSTM (medium) | Test perplexity | 79.7 | #38 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | Gal & Ghahramani (2016) - Variational LSTM (medium) | Validation perplexity | 81.9 | #38 of 43 | 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
Introduced by this paper: Embedding Dropout, Variational Dropout
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