Brief

Researchers have developed theoretical upper generalization bounds for neural oscillators, which are architectures combining second-order ordinary differential equations with multilayer perceptrons. These bounds, derived using the Rademacher complexity framework, quantify the generalization capacities for approximating causal operators and stable dynamical systems. The findings indicate that estimation errors scale polynomially with MLP sizes and time length, suggesting that regularization of MLP Lipschitz constants can enhance generalization, particularly with limited training data. AI