Reviews: On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective

This paper gives convergence guarantees for training neural networks via gradient descent. The approach consists of considering GD as an operator and of analyzing it through its eigenvalues. The interest of the analysis is focus on the overparameterized setting of such network. This is an interesting paper, with interesting theoretical results. It is clearly above the acceptance threshold.