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A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training

Abstract : In this paper, we develop a novel second-order method for training feed-forward neural nets. At each iteration, we construct a quadratic approximation to the cost function in a low-dimensional subspace. We minimize this approximation inside a trust region through a two-stage procedure: first inside the embedded positive curvature subspace, followed by a gradient descent step. This approach leads to a fast objective function decay, prevents convergence to saddle points, and alleviates the need for manually tuning parameters. We show the good performance of the proposed algorithm on benchmark datasets.
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Contributor : Emilie Chouzenoux <>
Submitted on : Tuesday, November 14, 2017 - 11:32:35 AM
Last modification on : Thursday, February 4, 2021 - 6:10:04 PM
Long-term archiving on: : Thursday, February 15, 2018 - 2:50:52 PM


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  • HAL Id : hal-01634538, version 1


Viacheslav Dudar, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet, Vladimir Semenov. A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training. 25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos Island, Greece. pp.291-295. ⟨hal-01634538⟩



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