Methods › Sequential › Recurrent Neural Networks › AWD-LSTM

ASGD Weight-Dropped LSTM

AWD-LSTM

52 papers tagged archive 2025-07-28

Introduced by Stephen Merity et al. in Regularizing and Optimizing LSTM Language Models

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ASGD Weight-Dropped LSTM, or AWD-LSTM, is a type of recurrent neural network that employs DropConnect for regularization, as well as NT-ASGD for optimization - non-monotonically triggered averaged SGD - which returns an average of last iterations of weights. Additional regularization techniques employed include variable length backpropagation sequences, variational dropout, embedding dropout, weight tying, independent embedding/hidden size, activation regularization and temporal activation regularization.

PaperSource

Papers archive 2025-07-28

30 shown of 52, 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.

Tasks archive 2025-07-28

20 shown of 65 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Language Modelling19
Language Modeling17
Transfer Learning16
Text Classification15
General Classification14
text-classification11
Sentiment Analysis9
Classification8
Language Identification4
Translation4
Word Embeddings4
Decision Making3
Hate Speech Detection3
Machine Translation3
Sentence3
Sentiment Classification3
Articles2
BIG-bench Machine Learning2
Image Classification2
Management2

Usage over time archive 2025-07-28

Papers per year tagged with AWD-LSTM: 2017 to 2025, peak 15 15 0 2017: 2 papers 2017 2018: 3 papers 2018 2019: 14 papers 2019 2020: 15 papers 2020 2021: 12 papers 2021 2022: 1 paper 2022 2023: 2 papers 2023 2024: 2 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (52 dated). Bars are counts, not a trend claim.

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

Recurrent Neural Networks

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