What is ensemble learning with neural networks?

Neural network ensembles combine multiple neural models to improve robustness and accuracy.

Neural network ensembles involve training multiple networks with different initializations, architectures, or data subsets. Their predictions are combined via averaging, voting, or stacking. This reduces variance in deep models, which are highly sensitive to initialization and data noise. Techniques include snapshot ensembles, deep ensemble training, and Monte Carlo dropout.