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Universal Transformer

18 papers tagged archive 2025-07-28

Introduced by Mostafa Dehghani et al. in Universal Transformers

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

The Universal Transformer is a generalization of the Transformer architecture. Universal Transformers combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. They also utilise a dynamic per-position halting mechanism.

PaperSource

Papers archive 2025-07-28

18 shown of 18, 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 43 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
Sentence4
Language Modeling3
Language Modelling3
Semantic Communication3
Sentence Similarity2
Text Generation2
Articles1
Automatic Speech Recognition1
Automatic Speech Recognition (ASR)1
Binary Classification1
Decoder1
Diagnostic1
Document Summarization1
Headline Generation1
Inductive Bias1
Instance Segmentation1
LAMBADA1
Learning to Execute1
ListOps1
Logical Sequence1

Usage over time archive 2025-07-28

Papers per year tagged with Universal Transformer: 2018 to 2024, peak 6 6 0 2018: 2 papers 2018 2019: 3 papers 2019 2020: 1 paper 2020 2021: 4 papers 2021 2022: 0 papers 2022 2023: 6 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (18 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

Autoregressive TransformersTransformers

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