Temporal Constraints for AI Generalization

This paper argues that imposing temporal constraints on deep learning models, inspired by biological systems, can improve generalization. It suggests that these constraints act as an inductive bias, shaping the network's dynamics to extract invariant features and reduce noise. The research highlights a 'transition' regime where generalization is maximized, emphasizing the importance of temporal integration and proper constraints in architecture design. This challenges the conventional approach of unconstrained optimization.