Methods › General › Normalization › Layer Normalization

Layer Normalization

24,980 papers tagged archive 2025-07-28

Introduced by Jimmy Lei Ba et al. in Layer Normalization

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

Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for RNNs and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with Transformer models.

We compute the layer normalization statistics over all the hidden units in the same layer as follows:

μˡ = 1/H∑ᴴᵢ₌₁aᵢˡ

σˡ = √(1/H∑ᴴᵢ₌₁(aᵢˡ-μˡ)²)

where H denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms μ and σ, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.

PaperSourceSee Code · CyberZHG/torch-layer-normalization

Papers archive 2025-07-28

30 shown of 24,980, 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 2,597 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 Modelling3,010
Language Modeling2,355
Retrieval1,818
Question Answering1,490
Decoder1,393
RAG1,355
Sentence1,334
Retrieval-augmented Generation1,176
Translation1,035
Machine Translation925
Semantic Segmentation862
Large Language Model841
Text Generation763
Image Classification720
Transfer Learning715
Object Detection668
Representation Learning622
Sentiment Analysis608
Classification597
object-detection597

Usage over time archive 2025-07-28

Papers per year tagged with Layer Normalization: 2016 to 2025, peak 6,979 6,979 0 2016: 2 papers 2016 2017: 26 papers 2017 2018: 130 papers 2018 2019: 1074 papers 2019 2020: 2146 papers 2020 2021: 3163 papers 2021 2022: 3357 papers 2022 2023: 5282 papers 2023 2024: 6979 papers 2024 2025: 2821 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (24,980 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

Normalization

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