Understanding the fundamental mechanism behind the success of deep neural networks is one of the key challenges in the modern machine learning literature. Despite numerous attempts, a solid theoretical analysis is yet to be developed. In this paper, we develop a novel unified framework to reveal a hidden regularization mechanism through the lens of convex optimization. We first show that the training of multiple threelayer ReLU subnetworks with weight decay regularization can be equivalently cast as a convex optimization problem in a higher dimensional space, where sparsity is enforced via a group `1 norm regularization. Consequently, ReLU networks can be interpreted as high dimensional feature selection methods. More importantly, we then prove that the equivalent convex problem can be globally optimized by a standard convex optimization solver with a polynomialtime complexity with respect to the number of samples and data dimension when the width of the network is fixed. Finally, we numerically validate our theoretical results via experiments involving both synthetic and real datasets.
Neural Networks are Convex Regularizers: Exact Polynomialtime Convex Optimization Formulations for TwoLayer Networks
We develop exact representations of training twolayer neural networks with rectified linear units (ReLUs) in terms of a single convex program with number of variables polynomial in the number of training samples and the number of hidden neurons. Our theory utilizes semiinfinite duality and minimum norm regularization. We show that ReLU networks trained with standard weight decay are equivalent to block `1 penalized convex models. Moreover, we show that certain standard convolutional linear networks are equivalent semidefinite programs which can be simplified to `1 regularized linear models in a polynomial sized discrete Fourier feature space.
 Award ID(s):
 1838179
 Publication Date:
 NSFPAR ID:
 10206903
 Journal Name:
 International Conference on Machine Learning
 Sponsoring Org:
 National Science Foundation
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