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While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal.
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Learning multiple layers of features from tiny images
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Efficient and robust automated machine learning
M. Feurer, A. Klein, K. Eggensperger, J. T. Springenberg, M. Blum, and F. Hutter · 2015
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B. Shahriari, K. Swersky, Z. Wang, R. Adams, and N. de Freitas · 2016
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Improved regularization of convolutional neural networks with cutout
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Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2017
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Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2017
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2018
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Multi-objective architecture search for cnns
T. Elsken, J. H. Metzen, and F. Hutter · 2018
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Practical hyperparameter optimization for deep learning
S. Falkner, A. Klein, and F. Hutter · 2018
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mixup: Beyond empirical risk minimization
Yann N. Dauphin David Lopez-Paz Hongyi Zhang, Moustapha Cisse · 2018
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On the state of the art of evaluation in neural language models
G. Melis, C. Dyer, and P. Blunsom · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
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Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, and Alexey Kurakin · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2017
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Algorithms for hyper-parameter optimization
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl
Cited in the paper.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
T. Domhan, J. T. Springenberg, and F. Hutter
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An efficient approach for assessing hyperparameter importance
F. Hutter, H. Hoos, and K. Leyton-Brown
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. Hoos, and K. Leyton-Brown
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Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2018
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Yoshihiro Yamada, Masakazu Iwamura, and Koichi Kise · 2018
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