Results for "per-parameter"

Popular optimizer combining momentum and per-parameter adaptive step sizes via first/second moment estimates.

Assigning labels per pixel (semantic) or per instance (instance segmentation) to map object boundaries.

Controls the size of parameter updates; too high diverges, too low trains slowly or gets stuck.

Number of samples per gradient update; impacts compute efficiency, generalization, and stability.

How many requests or tokens can be processed per unit time; affects scalability and cost.

Bayesian parameter estimation using the mode of the posterior distribution.

The shape of the loss function over parameter space.

Tradeoffs between many layers vs many neurons per layer.

Low-latency prediction per request.

Techniques that fine-tune small additional components rather than all weights to reduce compute and storage.

Using same parameters across different parts of a model.