Hessian Matrix

Neural networks can approximate any continuous function under certain conditions.

Allows gradients to bypass layers, enabling very deep networks.

Early architecture using learned gates for skip connections.

A narrow hidden layer forcing compact representations.

Adjusting learning rate over training to improve convergence.

Gradually increasing learning rate at training start to avoid divergence.

Limiting gradient magnitude to prevent exploding gradients.

Optimization using curvature information; often expensive at scale.