Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets. (arXiv:2201.02177v1 [cs.LG])

In this paper we propose to study generalization of neural networks on small
algorithmically generated datasets. In this setting, questions about data
efficiency, memorization, generalization, and speed of learning can be studied
in great detail. In some situations we show that neural networks learn through
a process of "grokking" a pattern in the data, improving generalization
performance from random chance level to perfect generalization, and that this
improvement in generalization can happen well past the point of overfitting. We
also …

arxiv datasets overfitting small