Scalar Input

Scalar input methods address the challenge of predicting outputs (scalar or functional) based on single numerical inputs, a fundamental problem across numerous scientific domains. Current research focuses on improving the efficiency and accuracy of algorithms like Sign-Perturbed Sums and regularization-based approaches for linear regression, as well as developing neural network architectures tailored for functional outputs from scalar inputs. These advancements are crucial for enhancing the precision and scalability of analyses in diverse fields, ranging from system identification and continual learning to data analysis and ranking systems.