Stable rank normalization for improved generalization in neural networks and GANs
Exciting new work on generalization bounds for neural networks (NN) given by Bartlett et al. (2017); Neyshabur et al. (2018) closely depend on two parameter- dependant quantities: the Lipschitz constant upper bound and the stable rank (a softer version of rank). Even though these bounds typically ha...
| Auteurs principaux: | , , |
|---|---|
| Format: | Conference item |
| Langue: | English |
| Publié: |
International Conference on Learning Representations
2020
|