Improving the classification accuracy of Oscillatory Neural Networks with the help of Max-Cut

Oscillatory neural networks (ONNs) are networks of fully coupled oscillators inspired by the Ising model. Whilst being meant for pattern retrieval and showing promise in hard optimization tasks, ONNs have struggled to emulate current state-of-the-art classification accuracies. This paper investigates a classification method utilizing Max-Cut.