Reviews: Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods

This paper studied a particular class of feedforward neural networks that can be trained globally optimal with a linear convergence rate using nonlinear spectral method. This method was applied to deep networks with one- and two-hidden layers. Experiments were conducted on a series of real world datasets. As stated by authors, the class of feedforward neural networks studied is restrictive and counterintuitive by imposing the non-negativity on the weights of network and maximizing the regularization of these weights. Moreover, the less popular activation function called generalized polynomial is required for the optimality condition. All these assumptions are not quite reasonable.