Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions

This provides a theoretical foundation for the efficiency of deep neural networks in approximating smooth functions, which is incremental as it builds on existing approximation theory.

The paper tackles the problem of approximating analytic functions in low dimensions with deep neural networks, proving that the convergence rate is exponential.