Zhihan Jin

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent

We give the first superpolynomial lower bounds for learning one-layer neural networks with respect to the Gaussian distribution for a broad class of algorithms. In the regression setting, we prove that gradient descent run on any classifier with respect to square loss will fail to achieve small test…