A Generalization Bound for Nearly-Linear Networks
<|reference_start|>A Generalization Bound for Nearly-Linear Networks: We consider nonlinear networks as perturbations of linear ones. Based on this approach, we present novel generalization bounds that become non-vacuous for networks that are close to being linear. The main advantage over the previous works which propose non-vacuous generalization bounds is that our bounds are a-priori: performing the actual training is not required for evaluating the bounds. To the best of our knowledge, they are the first non-vacuous generalization bounds for neural nets possessing this property.<|reference_end|>
@article{golikov2024a,
    title={A Generalization Bound for Nearly-Linear Networks},
    author={Eugene Golikov},
    journal={arXiv preprint arXiv:2407.06765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06765},
    primaryClass={cs.LG cs.AI stat.ML}
}