Results for "function approximation"

Fast approximation of costly simulations.

Neural networks can approximate any continuous function under certain conditions.

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

Optimization under uncertainty.

Learning a function from input-output pairs (labeled data), optimizing performance on predicting outputs for unseen inputs.

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

A parameterized function composed of interconnected units organized in layers with nonlinear activations.

The shape of the loss function over parameter space.

Sensitivity of a function to input perturbations.

Direction of steepest ascent of a function.

Stability proven via monotonic decrease of Lyapunov function.

Inferring reward function from observed behavior.

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

Nonlinear functions enabling networks to approximate complex mappings; ReLU variants dominate modern DL.

Constraining model outputs into a schema used to call external APIs/tools safely and deterministically.

Expected cumulative reward from a state or state-action pair.

Expected return of taking action in a state.

Probability of data given parameters.