Results for "nonlinear function"

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

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

Stability proven via monotonic decrease of Lyapunov function.

Models time evolution via hidden states.

An RNN variant using gates to mitigate vanishing gradients and capture longer context.

Monte Carlo method for state estimation.

Control that remains stable under model uncertainty.

Computing joint angles for desired end-effector pose.

Robots made of flexible materials.

System-level behavior arising from interactions.

Expected return of taking action in a state.

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

Probability of data given parameters.

Converts logits to probabilities by exponentiation and normalization; common in classification and LMs.

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

Lowest possible loss.

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

Direction of steepest ascent of a function.

Neural networks can approximate any continuous function under certain conditions.

Inferring reward function from observed behavior.

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

A parameterized mapping from inputs to outputs; includes architecture + learned parameters.

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

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

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

The shape of the loss function over parameter space.

Matrix of second derivatives describing local curvature of loss.

Combines value estimation (critic) with policy learning (actor).

Learns the score (∇ log p(x)) for generative sampling.

Matrix of first-order derivatives for vector-valued functions.