What is recorded here?
Used a memory-efficient representation of Householder-matrix products to parallelize delta-rule linear-transformer training across sequence positions.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
DeltaNet and the delta-rule fast-weight architecture precede this paper; the acquired contribution is efficient sequence-parallel training and scaling evidence, not invention of delta learning. Source-reported recall/quality gains are task-specific.
Read the original work
- Parallelizing Linear Transformers with the Delta Rule over Sequence Length
- Yang et al.: sequence-parallel training of delta-rule linear models — official method source
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People and systems
Songlin Yang; Bailin Wang; Yu Zhang; Yikang Shen; Yoon Kim
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
Yang et al.: sequence-parallel training of delta-rule linear models
Used a memory-efficient representation of Householder-matrix products to parallelize delta-rule linear-transformer training across sequence positions.
Scientific scope
DeltaNet and the delta-rule fast-weight architecture precede this paper; the acquired contribution is efficient sequence-parallel training and scaling evidence, not invention of delta learning. Source-reported recall/quality gains are task-specific.
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
Songlin Yang; Bailin Wang; Yu Zhang; Yikang Shen; Yoon Kim
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.