from OpenAI and Google researchers examines how neural networks generalize on small, algorithmically generated datasets.
a network significantly improves its generalization performance after a point of overfitting, achieving perfect generalization in certain cases.
This study is significant as it delves into the understanding of generalization in overparameterized neural networks beyond just memorizing finite training datasets
네트워크가 단순히 데이터셋을 암기하는 것을 넘어서 일반화 능력을 갖추게 되는 지점을 탐색하는 것
인간의 학습 과정과 매우 유사하다
