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arXiv · 1805.01532

Lifted Neural Networks

Armin Askari·Geoffrey Negiar·Rajiv Sambharya·Laurent El Ghaoui
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Abstract

We describe a novel family of models of multi- layer feedforward neural networks in which the activation functions are encoded via penalties in the training problem. Our approach is based on representing a non-decreasing activation function as the argmin of an appropriate convex optimiza- tion problem. The new framework allows for algo- rithms such as block-coordinate descent methods to be applied, in which each step is composed of a simple (no hidden layer) supervised learning problem that is parallelizable across data points and/or layers. Experiments indicate that the pro- posed models provide excellent initial guesses for weights for standard neural networks. In addi- tion, the model provides avenues for interesting extensions, such as robustness against noisy in- puts and optimizing over parameters in activation functions.

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Armin Askari, Geoffrey Negiar, Rajiv Sambharya, Laurent El Ghaoui. 2018-05-03. Lifted Neural Networks. https://arxiv.org/abs/1805.01532

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