A Generalization Bound for Nearly-Linear Networks

The article presents novel non-vacuous generalization bounds for nonlinear networks, derived by treating them as perturbations of linear networks. These bounds are a-priori, meaning they can be evaluated without the need for actual training, marking a significant advancement in generalization theory for neural networks. This development is crucial for practitioners as it provides a theoretical framework to assess model performance before training, potentially guiding more effective model design and selection.