Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Deep learning models are often successfully trained using gradient descent, despite the worst<lb>case hardness of the underlying non-convex optimization problem. The key question is then under<lb>what conditions can one prove that optimization will succeed. Here we provide a strong result<lb>of this kind. We consider a neural net with one hidden layer and a convolutional structure with<lb>no overlap and a ReLU activation function. For this architecture we show that learning is NP-<lb>complete in the general case, but that when the input distribution is Gaussian, gradient descent<lb>converges to the global optimum in polynomial time. To the best of our knowledge, this is the<lb>first global optimality guarantee of gradient descent on a convolutional neural network with ReLU<lb>activations.

Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs