Deep Ensemble

Deep ensembles leverage the power of multiple neural networks trained independently to improve prediction accuracy, robustness, and uncertainty quantification over single models. Current research focuses on enhancing efficiency through techniques like low-rank approximations, optimizing ensemble diversity and sharpness, and developing novel architectures such as hierarchical transformers and uncertainty-aware quantile regression models. This approach is proving valuable across diverse applications, from climate forecasting and medical diagnosis to autonomous driving and content moderation, by providing more reliable and trustworthy predictions, especially in situations with uncertainty or out-of-distribution data.