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Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 1902
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Building a large annotated corpus of english: The penn treebank
M. Marcus, M. Marcinkiewicz, and B. Santorini · 1993
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Gradient-based optimization of hyperparameters
Y. Bengio · 2000
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Algorithms for hyper-parameter optimization
J. Bergstra et al · 2011
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. Hoos, and K. Leyton-Brown · 2011
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
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Multi-task bayesian optimization
K. Swersky, J. Snoek, and R. Adams · 2013
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
T. Domhan, J. T. Springenberg, and F. Hutter · 2015
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Efficient and robust automated machine learning
M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter · 2015
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Non-stochastic best arm identification and hyperparameter optimization
K. Jamieson and A. Talwalkar · 2015
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Gradient-based hyperparameter optimization through reversible learning
D. Maclaurin, D. Duvenaud, and R. Adams · 2015
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Gaussian process bandit optimisation with multi-fidelity evaluations
K. Kandasamy, G. Dasarathy, J. B. Oliva, J. Schneider, and B. Póczos · 2016
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Tpot: A tree-based pipeline optimization tool for automating machine learning
R. S. Olson and J. H. Moore · 2016
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Network morphism
T. Wei, C. Wang, Y. Rui, and C. W. Chen · 2016
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
T. Devries and G. W. Taylor · 2017
Cited alongside, same era.
Google vizier: A service for black-box optimization
D. Golovin, B. Sonik, S. Moitra, G. Kochanski, J. Karro, and D.Sculley · 2017
Cited alongside, same era.
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
E. Hoffer, I. Hubara, and D. Soudry · 2017
Cited alongside, same era.
Population based training of neural networks
M. Jaderberg, V. Dalibard, S. Osindero, W. Czarnecki, J. Donahue, A. Razavi, O. Vinyals, T. Green, I. Dunning, K. Simonyan, et al · 2017
Cited alongside, same era.
Multi-fidelity bayesian optimisation with continuous approximations
K. Kandasamy, G. Dasarathy, J. Schneider, and B. Póczos · 2017
Auto-Keras: Efficient Neural Architecture Search with Network Morphism
H. Jin, Q. Song, and X. Hu · 2018
Later among the works it cites.
Neural Architecture Search with Bayesian Optimization and Optimal Transport
K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. Xing · 2018
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Neural Architecture Optimization
R. Luo, F. Tian, T. Qin, E. Chen, and T.-Y. Liu · 2018
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Regularizing and optimizing LSTM language models
S. Merity, N. Keskar, and R. Socher · 2018
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Efficient neural architecture search via parameters sharing
H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean · 2018
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Regularized Evolution for Image Classifier Architecture Search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
Later among the works it cites.
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Cited alongside, same era.
Fast bayesian optimization of machine learning hyperparameters on large datasets
A. Klein, S. Falkner, S. Bartels, P. Hennig, and F. Hutter · 2017
Cited alongside, same era.
Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2017
Cited alongside, same era.
DeepArchitect: Automatically Designing and Training Deep Architectures
R. Negrinho and G. Gordon · 2017
Cited alongside, same era.
Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. Leon Suematsu, Q. Le, and A. Kurakin · 2017
Cited alongside, same era.
Neural Architecture Search with Reinforcement Learning
B. Zoph and Q. V. Le · 2017
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
G. Bender, P.-J. Kindermans, B. Zoph, V. Vasudevan, and Q. Le · 2018
Cited alongside, same era.
Shakedrop regularization
Y. Yamada, M. Iwamura, and K. Kise · 2018
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Breaking the softmax bottleneck: A high-rank RNN language model
Z. Yang, Z. Dai, R. Salakhutdinov, and W. W. Cohen · 2018
Later among the works it cites.
ProxylessNAS: Direct neural architecture search on target task and hardware
H. Cai, L. Zhu, and S. Han · 2019
Closest in time.
Learnable embedding space for efficient neural architecture compression
S. Cao, X. Wang, and K. M. Kitani · 2019
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Massively parallel hyperparameter tuning
L. Li, K. G. Jamieson, A. Rostamizadeh, E. Gonina, M. Hardt, B. Recht, and A. Talwalkar · 2019
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DARTS: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
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SNAS: stochastic neural architecture search
S. Xie, H. Zheng, C. Liu, and L. Lin · 2019
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Graph hypernetworks for neural architecture search
C. Zhang, M. Ren, and R. Urtasun · 2019
Closest in time.