What is recorded here?
Scaled per-layer learning rates using weight and gradient norms to train image classifiers at much larger batch sizes while studying accuracy and hardware efficiency.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits data parallelism, warmup and SGD. ResNet/AlexNet results do not guarantee unchanged generalization at arbitrary batch sizes, datasets or architectures. Parallel-hardware and schedule choices are part of the reported speedup.
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People and systems
Yang You; Igor Gitman; Boris Ginsburg
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
LARS: layerwise rate scaling for large-batch image training
Scaled per-layer learning rates using weight and gradient norms to train image classifiers at much larger batch sizes while studying accuracy and hardware efficiency.
Scientific scope
Inherits data parallelism, warmup and SGD. ResNet/AlexNet results do not guarantee unchanged generalization at arbitrary batch sizes, datasets or architectures. Parallel-hardware and schedule choices are part of the reported speedup.
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
Yang You; Igor Gitman; Boris Ginsburg
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.