Gradient Noise

The shape of the loss function over parameter space.

A narrow minimum often associated with poorer generalization.

A wide basin often correlated with better generalization.

Adjusting learning rate over training to improve convergence.

Bayesian parameter estimation using the mode of the posterior distribution.

Optimization problems where any local minimum is global.

Optimization with multiple local minima/saddle points; typical in neural networks.

A point where gradient is zero but is neither a max nor min; common in deep nets.