Deep ReLU Network

Deep ReLU networks, characterized by their use of rectified linear unit activation functions, are a central focus in deep learning research, with current efforts concentrating on understanding their approximation capabilities, generalization properties, and optimization dynamics. Researchers are exploring various architectures, including deep operator networks and "nested" networks, and developing novel algorithms like component-based sketching to improve training efficiency and generalization performance. These investigations aim to provide a stronger theoretical foundation for the remarkable empirical success of deep ReLU networks, ultimately leading to more robust and efficient deep learning models for diverse applications.