On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
This provides a simpler proof for training deep neural networks, addressing a theoretical bottleneck in optimization for machine learning researchers.
The paper tackles the problem of proving global convergence for gradient descent in deep ReLU networks with square loss, showing that a single wide layer of linear, quadratic, or cubic width suffices, compared to prior requirements of at least Ω(N^8) width across all hidden layers.
We give a simple proof for the global convergence of gradient descent in training deep ReLU networks with the standard square loss, and show some of its improvements over the state-of-the-art. In particular, while prior works require all the hidden layers to be wide with width at least $Ω(N^8)$ ($N$ being the number of training samples), we require a single wide layer of linear, quadratic or cubic width depending on the type of initialization. Unlike many recent proofs based on the Neural Tangent Kernel (NTK), our proof need not track the evolution of the entire NTK matrix, or more generally, any quantities related to the changes of activation patterns during training. Instead, we only need to track the evolution of the output at the last hidden layer, which can be done much more easily thanks to the Lipschitz property of ReLU. Some highlights of our setting: (i) all the layers are trained with standard gradient descent, (ii) the network has standard parameterization as opposed to the NTK one, and (iii) the network has a single wide layer as opposed to having all wide hidden layers as in most of NTK-related results.