Papers › Pushing the bounds of dropout
Pushing the bounds of dropout
Gábor Melis, Charles Blundell, Tomáš Kočiský, Karl Moritz Hermann, Chris Dyer, Phil Blunsom
We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the original dropout objective. This discovery allows us to pick any model from this family after training, which leads to a substantial improvement on regularisation-heavy language modelling. The family includes models that compute a power mean over the sampled dropout masks, and their less stochastic subvariants with tighter and higher lower bounds than the fully stochastic dropout objective. We argue that since the deterministic subvariant's bound is equal to its objective, and the highest amongst these models, the predominant view of it as a good approximation to MC averaging is misleading. Rather, deterministic dropout is the best available approximation to the true objective.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | Penn Treebank (Word Level) | 2-layer skip-LSTM + dropout tuning | Params | 24M | #24 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | 2-layer skip-LSTM + dropout tuning | Test perplexity | 55.3 | #24 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | 2-layer skip-LSTM + dropout tuning | Validation perplexity | 57.1 | #24 of 43 | Archive leaderboard | report |
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