InforLorV4, Main, Curation, bibRecord, 000578

Fast DNN training based on auxiliary function technique

Identifieur interne : 000578 ( Main/Curation ); précédent : 000577; suivant : 000579

Fast DNN training based on auxiliary function technique

Auteurs : Dung T. Tran [France] ; Nobutaka Ono [Japon] ; Emmanuel Vincent [France]

Source :

RBID : Hal:hal-01107809

English descriptors

mix :
- IndexTerms—DNN, adaptivelearningrate, auxiliaryfunctiontechnique, back-propagation, gradientdescent.

Abstract

Deep neural networks (DNN) are typically optimized with stochastic gradient descent (SGD) using a fixed learning rate or an adaptivelearning rate approach (ADAGRAD). In this paper, we introduce a new learning rule for neural networks that is based on an auxiliaryfunction technique without parameter tuning. Instead of minimizing the objective function, a quadratic auxiliary function is recursivelyintroduced layer by layer which has a closed-form optimum. We prove the monotonic decrease of the new learning rule. Our experiments show that the proposed algorithm converges faster and to a better local minimum than SGD. In addition, we propose a combination of the proposed learning rule and ADAGRAD which further accelerates convergence. Experimental evaluation on the MNIST database shows the benefit of the proposed approach in terms of digit recognition accuracy.

Url:

https://hal.inria.fr/hal-01107809

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Le document en format XML

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<date type="start">1939-10-19</date>
<desc><address><country key="FR"></country>
</address>
<ref type="url">http://www.cnrs.fr/</ref>
</desc>
</org>
</tutelle>
</tutelles>
</hal:affiliation>
<country>France</country>
<placeName><settlement type="city">Nancy</settlement>
<settlement type="city">Metz</settlement>
<region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
</placeName>
<orgName type="university">Université de Lorraine</orgName>
</affiliation>
</author>
</analytic>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>IndexTerms—DNN</term>
<term>adaptivelearningrate</term>
<term>auxiliaryfunctiontechnique</term>
<term>back-propagation</term>
<term>gradientdescent</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Deep neural networks (DNN) are typically optimized with stochastic gradient descent (SGD) using a fixed learning rate or an adaptivelearning rate approach (ADAGRAD). In this paper, we introduce a new learning rule for neural networks that is based on an auxiliaryfunction technique without parameter tuning. Instead of minimizing the objective function, a quadratic auxiliary function is recursivelyintroduced layer by layer which has a closed-form optimum. We prove the monotonic decrease of the new learning rule. Our experiments show that the proposed algorithm converges faster and to a better local minimum than SGD. In addition, we propose a combination of the proposed learning rule and ADAGRAD which further accelerates convergence. Experimental evaluation on the MNIST database shows the benefit of the proposed approach in terms of digit recognition accuracy.</div>
</front>
</TEI>
</record>

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Fast DNN training based on auxiliary function technique

Fast DNN training based on auxiliary function technique

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