weight
A weight is a learned scalar or tensor parameter that modulates the contribution of an input or intermediate signal through a linear or affine transformation, determining how strongly it influences the model’s output.
In neural networks, weights connect units within and across layers and are updated during training by gradient-based optimization.
They’re typically initialized from specific distributions and may be regularized with techniques like weight decay. They can be shared across positions as in convolutions or attention projections.
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By Leodanis Pozo Ramos • Updated June 29, 2026
