Geometric structure of shallow neural networks and constructive ${\mathcal L}^2$ cost minimization

Researchers developed a new approach to minimize cost functions in shallow ReLU neural networks through explicit construction rather than gradient descent. The study provides mathematical upper bounds for cost minimization and characterizes the geometric structure of network minimizers in classification tasks.