Bounding the Approximation Capabilities of Norm-Constrained Deep Neural Networks

This ArXiv paper likely delves into the theoretical underpinnings of deep learning, specifically how constraints on the network's weights affect its ability to approximate functions. The research could contribute to a better understanding of model generalization and the design of more efficient and robust neural network architectures.

• •Focuses on the approximation capabilities of deep neural networks.
• •Investigates the impact of norm constraints on these capabilities.
• •Provides theoretical bounds on approximation performance.