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The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically.
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The cascade-correlation learning architecture
S. E. Fahlman and C. Lebiere · 1990
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A comparative analysis of selection schemes used in genetic algorithms
D. E. Goldberg and K. Deb · 1991
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An evolutionary algorithm that constructs recurrent neural networks
P. J. Angeline, G. M. Saunders, and J. B. Pollack · 1994
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Evolving artificial neural networks
X. Yao · 1999
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Evolving neural networks through augmenting topologies
K. O. Stanley and R. Miikkulainen · 2002
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Real-time neuroevolution in the nero video game
K. O. Stanley, B. D. Bryant, and R. Miikkulainen · 2005
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Alps: the age-layered population structure for reducing the problem of premature convergence
G. S. Hornby · 2006
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Neuroevolution: from architectures to learning
D. Floreano, P. Dürr, and C. Mattiussi · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant
J. P. Simmons, L. D. Nelson, and U. Simonsohn · 2011
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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Multi-column deep neural networks for image classification
D. Ciregan, U. Meier, and J. Schmidhuber · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. Le Cun, and R. Fergus · 2013
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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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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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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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Towards automatically-tuned neural networks
H. Mendoza, A. Klein, M. Feurer, J. T. Springenberg, and F. Hutter · 2016
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Convolutional neural fabrics
S. Saxena and J. Verbeek · 2016
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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
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Designing neural network architectures using reinforcement learning
A genetic programming approach to designing convolutional neural network architectures
M. Suganuma, S. Shirakawa, and T. Nagao · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Genetic CNN
L. Xie and A. Yuille · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Polynet: A pursuit of structural diversity in very deep networks
X. Zhang, Z. Li, C. C. Loy, and D. Lin · 2017
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Smash: one-shot model architecture search through hypernetworks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2018
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B. Baker, O. Gupta, N. Naik, and R. Raskar · 2017
Cited alongside, same era.
Accelerating neural architecture search using performance prediction
B. Baker, O. Gupta, R. Raskar, and N. Naik · 2017
Cited alongside, same era.
Dual path networks
Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng · 2017
Cited alongside, same era.
Adanet: Adaptive structural learning of artificial neural networks
C. Cortes, X. Gonzalvo, V. Kuznetsov, M. Mohri, and S. Yang · 2017
Cited alongside, same era.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
T. Domhan, J. T. Springenberg, and F. Hutter · 2017
Cited alongside, same era.
Simple and efficient architecture search for convolutional neural networks
T. Elsken, J.-H. Metzen, and F. Hutter · 2017
Cited alongside, same era.
Efficient architecture search by network transformation
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang · 2018
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Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark
C. Coleman, D. Kang, D. Narayanan, L. Nardi, T. Zhao, J. Zhang, P. Bailis, K. Olukotun, C. Re, and M. Zaharia · 2018
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Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
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Neural architecture search: A survey
T. Elsken, J. H. Metzen, and F. Hutter · 2018
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Deep reinforcement learning that matters
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen · 2018
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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 · 2018
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Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2018
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Faster discovery of neural architectures by searching for paths in a large model
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
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Practical network blocks design with q-learning
Z. Zhong, J. Yan, and C.-L. Liu · 2018
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
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