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arXiv · 1910.06995

Reduced-Order Modeling of Deep Neural Networks

Julia Gusak·Talgat Daulbaev·Evgeny Ponomarev·Andrzej Cichocki·Ivan Oseledets
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Abstract

We introduce a new method for speeding up the inference of deep neural networks. It is somewhat inspired by the reduced-order modeling techniques for dynamical systems.The cornerstone of the proposed method is the maximum volume algorithm. We demonstrate efficiency on neural networks pre-trained on different datasets. We show that in many practical cases it is possible to replace convolutional layers with much smaller fully-connected layers with a relatively small drop in accuracy.

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Julia Gusak, Talgat Daulbaev, Evgeny Ponomarev, Andrzej Cichocki, Ivan Oseledets. 2019-10-15. Reduced-Order Modeling of Deep Neural Networks. https://arxiv.org/abs/1910.06995

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