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A training technique that normalizes the inputs to each layer, allowing deeper networks to train faster and more stably.
Ioffe & Szegedy (2015) introduced batch normalization, which standardizes each layer's inputs using the statistics of the current mini-batch. This reduces the shifting of internal distributions during training, letting practitioners use higher learning rates and train much deeper networks reliably.
Related normalization schemes (such as layer normalization) are a standard component inside Transformers.
Primary, peer-reviewed and archival sources for this definition.
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