Hyper-parameter optimization of deep convolutional networks for object recognition
Published: Sep 1, 2015
Abstract
Recently sequential model based optimization (SMBO) has emerged as a promising hyper-parameter optimization strategy in machine learning. In this work, we investigate SMBO to identify architecture hyper-parameters of deep convolution networks (DCNs) object recognition. We propose a simple SMBO strategy that starts from a set of random initial DCN architectures to generate new architectures, which on training perform well on a given dataset....
Paper Details
Title
Hyper-parameter optimization of deep convolutional networks for object recognition
Published Date
Sep 1, 2015
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