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Deep ensembles are a machine learning technique that combines multiple independently trained neural networks to improve predictive performance and uncertainty estimation. They are widely used to enhance model robustness and provide more reliable confidence measures in predictions.
Batch ensemble is a machine learning technique designed to improve the efficiency and scalability of ensemble models by sharing parameters across multiple ensemble members, enabling more practical uncertainty estimation and robustness in neural networks.
Model averaging, also known as model soups, is a technique in machine learning that combines multiple trained models or their parameters to improve performance or robustness. It involves averaging the weights of different models to create a single, consolidated model representation.
Hyper-deep ensembles are advanced machine learning models that combine multiple deep neural networks to improve predictive performance, robustness, and uncertainty estimation. They extend traditional ensemble methods by leveraging very large or highly complex models in a coordinated manner.