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In this paper, we compare the three most popular algorithms for hyperparameter optimization (Grid Search, Random Search, and Genetic Algorithm) and attempt to use them for neural architecture search (NAS).
Adanet: A scalable and flexible framework for automatically learning ensembles
Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, and Corinna Cortes · 1905
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Convolutional neural network hyper-parameters optimization based on genetic algorithms
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Efficient neural architecture search via parameter sharing
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Bayesian optimization using deep gaussian processes, 2019
Ali Hebbal, Loic Brevault, Mathieu Balesdent, El-Ghazali Talbi, and Nouredine Melab · 2019
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Convolutional neural networks
Jianxin Wu · 2019
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Neural architecture search: A survey, 2018
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
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Deep residual learning for image recognition, 2015a
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, 2015b
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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