Toward Optimal Run Racing: Application to Deep Learning Calibration

Abstract : This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand. The notoriously expensive calibration problem is optimally reduced by detecting and early stopping non-optimal runs. The theoretical contribution regards the optimality guarantees within the multiple hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki benchmarks demonstrate the relevance of the approach with a principled and consistent improvement on the state of the art with no extra hyper-parameter.
Type de document :
Pré-publication, Document de travail
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Contributeur : Marc Schoenauer <>
Soumis le : mardi 14 novembre 2017 - 09:27:48
Dernière modification le : jeudi 11 janvier 2018 - 06:20:12

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  • HAL Id : hal-01634381, version 1
  • ARXIV : 1706.03199


Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michèle Sebag, et al.. Toward Optimal Run Racing: Application to Deep Learning Calibration. 2017. 〈hal-01634381〉



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