Regression Network

Regression networks, a type of artificial neural network, aim to model the relationship between input and output variables by learning complex, non-linear functions. Current research emphasizes improving training stability, handling heterogeneous data (e.g., combining classification and regression tasks), and generating reliable prediction intervals alongside point estimates, often employing techniques like natural gradient descent, task consolidation, and conformal prediction. These advancements enhance the accuracy, robustness, and interpretability of regression networks, finding applications in diverse fields such as environmental risk assessment, material science, and financial forecasting.