ReLU Network

ReLU networks, a class of neural networks utilizing the rectified linear unit activation function, are a central focus in deep learning research, with current efforts concentrating on understanding their theoretical properties, improving training efficiency, and enhancing their interpretability. Research explores various aspects, including approximation capabilities, generalization behavior (especially concerning benign overfitting), and the impact of network architecture (depth, width, sparsity) on performance. These investigations are crucial for advancing both the theoretical foundations of deep learning and the development of more efficient and reliable machine learning applications across diverse fields.