Layer ReLU Network

Layer ReLU networks, utilizing the rectified linear unit activation function, are a central focus in neural network research, with current efforts concentrating on understanding their approximation capabilities, optimization challenges, and generalization properties. Investigations explore various architectures, including deep and shallow networks, recurrent networks, and those incorporating techniques like Bayesian methods and quadratic formulations for specific tasks such as combinatorial optimization. These studies aim to improve both the theoretical understanding of ReLU networks and their practical application in diverse fields, ranging from solving partial differential equations to image classification and beyond.