Title: Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs,
Abstract: Deep learning models are often successfully trained using gradient descent,
despite the worst case hardness of the underlying non-convex optimization
problem. The key question is then under what conditions can one prove that
optimization will succeed. Here we provide a strong result of this kind. We
consider a neural net with one hidden layer and a convolutional structure with
no overlap and a ReLU activation function. For this architecture we show that
learning is NP-complete in the general case, but that when the input
distribution is Gaussian, gradient descent converges to the global optimum in
polynomial time. To the best of our knowledge, this is the first global
optimality guarantee of gradient descent on a convolutional neural network with
ReLU activations.