Ensemble Neural Network

Ensemble neural networks combine multiple individual neural networks to improve prediction accuracy, robustness, and uncertainty quantification compared to single models. Current research focuses on developing efficient ensemble architectures, such as those employing parallel subnetworks, cascaded inference, or learnable weighting schemes, and applying them to diverse tasks including image classification, medical image segmentation, and time series forecasting. This approach is significant because it enhances model reliability and generalizability across various domains, leading to more trustworthy and effective applications in fields ranging from healthcare to robotics.