Methods › General › Initialization › T-Fixup

T-Fixup

2 papers tagged archive 2025-07-28

Introduced by Xiao Shi Huang et al. in Improving Transformer Optimization Through Better Initialization

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

T-Fixup is an initialization method for Transformers that aims to remove the need for layer normalization and warmup. The initialization procedure is as follows:

PaperSource

Papers archive 2025-07-28

2 shown of 2, 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

10 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
Decoder1
Language Modeling1
Language Modelling1
Machine Translation1
Reading Comprehension1
SQL Parsing1
Semantic Parsing1
Text to SQL1
Text-To-SQL1
Translation1

Usage over time archive 2025-07-28

Papers per year tagged with T-Fixup: 2020 to 2020, peak 2 2 0 2020: 2 papers 2020
Papers per year the archive tags with this method, by the paper's archive date (2 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

Initialization

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