Methods › Sequential › Recurrent Neural Networks › mLSTM

Multiplicative LSTM

mLSTM

7 papers tagged archive 2025-07-28

Introduced by Ben Krause et al. in Multiplicative LSTM for sequence modelling

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

A Multiplicative LSTM (mLSTM) is a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network (mRNN) architectures. The mRNN and LSTM architectures can be combined by adding connections from the mRNN’s intermediate state mₜ to each gating units in the LSTM.

PaperSource

Papers archive 2025-07-28

7 shown of 7, 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

9 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 Modelling6
Language Modeling5
Mamba3
Density Estimation1
GPU1
Math1
Mixture-of-Experts1
Re-Ranking1
State Space Models1

Usage over time archive 2025-07-28

Papers per year tagged with mLSTM: 2016 to 2025, peak 4 4 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 2 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (7 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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