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.