Results for "parameter sensitivity"

A conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).

Error due to sensitivity to fluctuations in the training dataset.

Sensitivity of a function to input perturbations.

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

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

Bayesian parameter estimation using the mode of the posterior distribution.

The shape of the loss function over parameter space.

Small prompt changes cause large output changes.

Ability to correctly detect disease.

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.