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Neural Architecture Search (NAS) is a research field concerned with utilizing optimization algorithms to design optimal neural network architectures.
Forming neural networks through efficient and adaptive coevolution
D. E. Moriarty and R. Miikkulainen · 1997
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Evolving neural networks through augmenting topologies
K. O. Stanley and R. Miikkulainen · 2002
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Ant colony optimization
M. Dorigo, M. Birattari, and T. Stutzle · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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An overview of gradient descent optimization algorithms
S. Ruder · 2016
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Network morphism
T. Wei, C. Wang, Y. Rui, and C. W. Chen · 2016
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
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Smash: One-shot model architecture search through hypernetworks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2017
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A downsampled variant of imagenet as an alternative to the cifar datasets
P. Chrabaszcz, I. Loshchilov, and F. Hutter · 2017
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Simple and efficient architecture search for convolutional neural networks
T. Elsken, J.-H. Metzen, and F. Hutter · 2017
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Playing fps games with deep reinforcement learning
G. Lample and D. S. Chaplot · 2017
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Deep transfer learning with joint adaptation networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2017
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Efficient architecture search by network transformation
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang · 2018
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Dpp-net: Device-aware progressive search for pareto-optimal neural architectures
J.-D. Dong, A.-C. Cheng, D.-C. Juan, W. Wei, and M. Sun · 2018
Cited alongside, same era.
Neural architecture search with bayesian optimisation and optimal transport
K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. P. Xing · 2018
Cited alongside, same era.
Evolutionary architecture search for deep multitask networks
J. Liang, E. Meyerson, and R. Miikkulainen · 2018
Cited alongside, same era.
Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2018
Cited alongside, same era.
Neural architecture optimization
R. Luo, F. Tian, T. Qin, E. Chen, and T.-Y. Liu · 2018
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
G. Bender · 2019
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Deepswarm: Optimising convolutional neural networks using swarm intelligence
E. Byla and W. Pang · 2019
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Auto-keras: An efficient neural architecture search system
H. Jin, Q. Song, and X. Hu · 2019
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Neural style transfer: A review
Y. Jing, Y. Yang, Z. Feng, J. Ye, Y. Yu, and M. Song · 2019
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Best practices for scientific research on neural architecture search
M. Lindauer and F. Hutter · 2019
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H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
Cited alongside, same era.
Improving deep learning with generic data augmentation
L. Taylor and G. Nitschke · 2018
Cited alongside, same era.
Snas: stochastic neural architecture search
S. Xie, H. Zheng, C. Liu, and L. Lin · 2018
Cited alongside, same era.
Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
A. Zela, A. Klein, S. Falkner, and F. Hutter · 2018
Cited alongside, same era.
Graph hypernetworks for neural architecture search
C. Zhang, M. Ren, and R. Urtasun · 2018
Cited alongside, same era.
Practical block-wise neural network architecture generation, 2018
Z. Zhong, J. Yan, W. Wu, J. Shao, and C.-L. Liu · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
Cited alongside, same era.
H. Liu, K. Simonyan, and Y. Yang · 2019
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Evolving deep neural networks
R. Miikkulainen, J. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, H. Shahrzad, A. Navruzyan, N. Duffy, et al · 2019
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le · 2019
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Nas-bench-101: Towards reproducible neural architecture search
C. Ying, A. Klein, E. Real, E. Christiansen, K. Murphy, and F. Hutter · 2019
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Nas-bench-102: Extending the scope of reproducible neural architecture search
X. Dong and Y. Yang · 2020
Closest in time.
Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search
A. Zela, J. Siems, and F. Hutter · 2020
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