Evan ShelhamerFaculty Member · Vector InstituteResearcher↗
Researcher · Academic
University of TorontoAssociate Professor
Vector InstituteFaculty Member
CIFARCanada CIFAR AI Chair
Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Adam automatically adjusts learning rates to guide neural-network training.
Layer normalization stabilizes training by rescaling signals within each example.
Kingma and Ba introduce Adam, which adapts the size of updates to individual model parameters during training.
The original Transformer uses layer normalization in its encoder and decoder (section 3.1) and Adam for training (section 5.3), citing Ba as co-author of both methods.
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Reviewed August 7, 2026 · Checked for accuracy and new information.