Ensemble Model

Ensemble models combine predictions from multiple individual models to improve overall accuracy, robustness, and uncertainty quantification. Current research focuses on developing efficient ensemble methods, particularly for large-scale datasets and resource-constrained environments, exploring diverse architectures like deep neural networks, graph convolutional networks, and recurrent neural networks, and employing techniques such as weight averaging, distillation, and adaptive training strategies. This approach is proving valuable across diverse fields, enhancing performance in tasks ranging from medical image analysis and materials science to financial forecasting and natural language processing. The resulting improvements in prediction accuracy and uncertainty estimation have significant implications for various scientific and practical applications.