______________ is a technique used in training neural networks where multiple models are trained and combined to improve performance and robustness

The technique you are referring to is known as Ensemble Learning. Ensemble learning is used in training neural networks where multiple models (or learners) are trained and subsequently combined to improve overall performance and robustness. The idea behind ensemble methods is that by combining several models, the ensemble can achieve better predictive performance than any single model could on its own. Famous techniques in ensemble learning include bagging (like Random Forests) and boosting (like AdaBoost or Gradient Boosting). These methods work by leveraging the strengths of individual models and compensating for their weaknesses, leading to more accurate and reliable predictions.