Order of Addition

"Order of addition" research explores how the sequence of operations or components affects outcomes across diverse fields. Current work focuses on optimizing computational efficiency by replacing multiplications with additions in neural networks (e.g., using Add-MLPs and Shift-MLPs) and improving model generalization through novel training methodologies (e.g., autoregressive techniques for number addition). These advancements have significant implications for energy-efficient computing, improved model performance in tasks like point cloud classification and anomaly detection, and more efficient algorithms for complex calculations such as Shapley value computation.