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Communication Dans Un Congrès Année : 2020

A Random Matrix Analysis of Learning with α-Dropout

Résumé

This article studies a one hidden layer neural network with generalized Dropout (α-Dropout), where the dropped out features are replaced with an arbitrary value α. Specifically, under a large dimensional data and network regime, we provide the generalization performances for this network on a binary classification problem. We notably demonstrate that a careful choice of α = 0 can drastically improve the generalization performances of the classifier.
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Dates et versions

hal-02971211 , version 1 (19-10-2020)

Identifiants

  • HAL Id : hal-02971211 , version 1

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Mohamed El Amine Seddik, Romain Couillet, Mohamed Tamaazousti. A Random Matrix Analysis of Learning with α-Dropout. ICML 2020 Workshop Artemiss - 1st Workshop on the Art of Learning with Missing Values, Jul 2020, Virtual, France. ⟨hal-02971211⟩
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