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Neural architecture search (NAS) methods have been proposed to release human experts from tedious architecture engineering.
A comparative analysis of selection schemes used in genetic algorithms
D. E. Goldberg and K. Deb · 1990
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Multi-objective optimization
K. Deb · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Net2net: Accelerating learning via knowledge transfer
T. Chen, I. J. Goodfellow, and J. Shlens · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
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Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
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Improved regularization of convolutional neural networks with cutout
T. Devries and G. W. Taylor · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2017
Cited alongside, same era.
R. Miikkulainen, J. Z. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, H. Shahrzad, A. Navruzyan, N. Duffy, and B. Hodjat · 2017
Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. 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
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M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen · 2018
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M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen · 2018
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Mnasnet: Platform-aware neural architecture search for mobile
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Cited alongside, same era.
A deeper look at dataset bias
T. Tommasi, N. Patricia, B. Caputo, and T. Tuytelaars · 2017
Cited alongside, same era.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
Cited alongside, same era.
Progressive neural architecture search
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. L. Yuille, J. Huang, and K. Murphy · 2018
Cited alongside, same era.
NSGA-NET: A multi-objective genetic algorithm for neural architecture search
Z. Lu, I. Whalen, V. Boddeti, Y. D. Dhebar, K. Deb, E. D. Goodman, and W. Banzhaf · 2018
Cited alongside, same era.
M. Tan, B. Chen, R. Pang, V. Vasudevan, and Q. V. Le · 2018
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Transfer learning with neural automl
C. Wong, N. Houlsby, Y. Lu, and A. Gesmundo · 2018
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Practical block-wise neural network architecture generation
Z. Zhong, J. Yan, W. Wu, J. Shao, and C.-L. Liu · 2018
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ProxylessNAS: Direct neural architecture search on target task and hardware
H. Cai, L. Zhu, and S. Han · 2019
Closest in time.
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
Closest in time.