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In neural architecture search (NAS), the space of neural network architectures is automatically explored to maximize predictive accuracy for a given task.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 1902
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Empirical Bayes: Past, present and future
B. P. Carlin and T. A. Louis · 2000
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Reweighted wake-sleep
J. Bornschein and Y. Bengio · 2015
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Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Empirical evaluation of rectified activations in convolutional network
B. Xu, N. Wang, T. Chen, and M. Li · 2015
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Designing neural network architectures using reinforcement learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
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.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
Cited alongside, same era.
Deeparchitect: Automatically designing and training deep architectures
R. Negrinho and G. Gordon · 2017
Cited alongside, same era.
DARTS: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2018
Later among the works it cites.
Neural architecture optimization
R. Luo, F. Tian, T. Qin, E. Chen, and T.-Y. Liu · 2018
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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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Mnasnet: Platform-aware neural architecture search for mobile
M. Tan, B. Chen, R. Pang, V. Vasudevan, and Q. V. Le · 2018
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L. Xie and A. Yuille · 2017
Cited alongside, same era.
Shufflenet: An extremely efficient convolutional neural network for mobile devices. arxiv 2017
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
Cited alongside, same era.
Revisiting reweighted wake-sleep
T. A. Le, A. R. Kosiorek, N. Siddharth, Y. W. Teh, and F. Wood · 2018
Cited alongside, same era.
Efficient architecture search by network transformation
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang
Cited in the paper.
Path-level network transformation for efficient architecture search
H. Cai, J. Yang, W. Zhang, S. Han, and Y. Yu
Cited in the paper.
Progressive neural architecture search
C. Liu, B. Zoph, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy
Cited in the paper.
Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu
Cited in the paper.
Later among the works it cites.
Practical block-wise neural network architecture generation
Z. Zhong, J. Yan, W. Wu, J. Shao, and C.-L. Liu · 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.
SNAS: stochastic neural architecture search
S. Xie, H. Zheng, C. Liu, and L. Lin · 2019
Closest in time.