Results for "loss"

The shape of the loss function over parameter space.

The learned numeric values of a model adjusted during training to minimize a loss function.

A scalar measure optimized during training, typically expected loss over data, sometimes with regularization terms.

Minimizing average loss on training data; can overfit when data is limited or biased.

Iterative method that updates parameters in the direction of negative gradient to minimize loss.

Reducing numeric precision of weights/activations to speed inference and reduce memory with acceptable accuracy loss.

Matrix of second derivatives describing local curvature of loss.

Lowest possible loss.

Loss of old knowledge when learning new tasks.

Maximum expected loss under normal conditions.

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

Penalizes confident wrong predictions heavily; standard for classification and language modeling.