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Contributed Video: Convex Programs for Global Optimization of Convolutional Neural Networks in Polynomial-Time, Tolga Ergen
Tolga Ergen
Event URL: https://opt-ml.org/papers/2020/paper_39.pdf »
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. Particularly, we develop a convex analytic framework utilizing semi-infinite duality to obtain equivalent convex optimization problems for two-layer CNNs, where convex problems are regularized by the sum of $\ell_2$ norms of variables.
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. Particularly, we develop a convex analytic framework utilizing semi-infinite duality to obtain equivalent convex optimization problems for two-layer CNNs, where convex problems are regularized by the sum of $\ell_2$ norms of variables.
Author Information
Tolga Ergen (Stanford University)
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