 The researchers developed a new method to automatically generate neural network architectures for EG-based sleep stage classification. This method uses a bi-level optimization approach to approximate the original optimization problem which reduces the computational complexity while maintaining high accuracy. The resulting models achieved an average accuracy of 82.7%, 80% and 81.9% on three different datasets. This article was authored by Gang Wei Kong, Chang Li, Hu Peng and others.