What is recorded here?
Combined modified residual normalization with depth-dependent initialization to stabilize very deep Transformer training, including reported thousand-layer experiments.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Source-reported analysis and training results, not an independently audited general convergence theorem. Inherits residual networks and normalization; depth scaling does not alone guarantee useful task performance.
Read the original work
- DeepNet: Scaling Transformers to 1,000 Layers
- Wang et al.: DeepNorm residual scaling and initialization — official project source
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Hongyu Wang; Shuming Ma; Li Dong; Shaohan Huang; Dongdong Zhang; Furu Wei
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Wang et al.: DeepNorm residual scaling and initialization
Combined modified residual normalization with depth-dependent initialization to stabilize very deep Transformer training, including reported thousand-layer experiments.
Scientific scope
Source-reported analysis and training results, not an independently audited general convergence theorem. Inherits residual networks and normalization; depth scaling does not alone guarantee useful task performance.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Hongyu Wang; Shuming Ma; Li Dong; Shaohan Huang; Dongdong Zhang; Furu Wei
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.