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Three features are crucial for sequential forecasting and generation models: tractability, expressiveness, and theoretical backing. While neural autoregressive models are relatively tractable and offer powerful predictive and generative capabilities, they often have complex optimization landscapes, and their theoretical properties are not well understood. To address these issues, we present convex formulations of autoregressive models with one hidden layer. Specifically, we prove an exact equivalence between these models and constrained, regularized logistic regression by using semiinfinite duality to embed the data matrix onto a higher dimensional space and introducing inequality constraints. To make this formulation tractable, we approximate the constraints using a hinge loss or drop them altogether. Furthermore, we demonstrate faster training and competitive performance of these implementations compared to their neural network counterparts on a variety of data sets. Consequently, we introduce techniques to derive tractable, expressive, and theoreticallyinterpretable models that are nearly equivalent to neural autoregressive models.

We study training of Convolutional Neural Networks (CNNs) with ReLU activations and introduce exact convex optimization formulations with a polynomial complexity with respect to the number of data samples, the number of neurons, and data dimension. More specifically, we develop a convex analytic framework utilizing semiinfinite duality to obtain equivalent convex optimization problems for several two and threelayer CNN architectures. We first prove that twolayer CNNs can be globally optimized via an `2 norm regularized convex program. We then show that multilayer circular CNN training problems with a single ReLU layer are equivalent to an `1 regularized convex program that encourages sparsity in the spectral domain. We also extend these results to threelayer CNNs with two ReLU layers. Furthermore, we present extensions of our approach to different pooling methods, which elucidates the implicit architectural bias as convex regularizers.

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.

We consider nonconvex training of shallow neural networks and introduce a convex relaxation approach with theoretical guarantees. For the single neuron case, we prove that the relaxation preserves the location of the global minimum under a planted model assumption. Therefore, a globally optimal solution can be efficiently found via a gradient method. We show that gradient descent applied on the relaxation always outperforms gradient descent on the original nonconvex loss with no additional computational cost. We then characterize this relaxation as a regularizer and further introduce extensions to multineuron single hidden layer networks.

We develop a convex analytic framework for ReLU neural networks which elucidates the inner workings of hidden neurons and their function space characteristics. We show that neural networks with rectified linear units act as convex regularizers, where simple solutions are encouraged via extreme points of a certain convex set. For one dimensional regression and classification, as well as rankone data matrices, we prove that finite twolayer ReLU networks with norm regularization yield linear spline interpolation. We characterize the classification decision regions in terms of a closed form kernel matrix and minimum L1 norm solutions. This is in contrast to Neural Tangent Kernel which is unable to explain neural network predictions with finitely many neurons. Our convex geometric description also provides intuitive explanations of hidden neurons as auto encoders. In higher dimensions, we show that the training problem for twolayer networks can be cast as a finite dimensional convex optimization problem with infinitely many constraints. We then provide a family of convex relaxations to approximate the solution, and a cuttingplane algorithm to improve the relaxations. We derive conditions for the exactness of the relaxations and provide simple closed form formulas for the optimal neural network weights in certain cases. We alsomore »