A Random Matrix Analysis of Learning with α-Dropout

May 16, 2020 Submission readers: everyone
  • Abstract: This article studies a single 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 different from 0 can drastically improve the generalization performances of the classifier.
  • Keywords: random matrix theory, dropout, zero imputation
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