Fetching the paper…
Reading the bibliography…
This paper addresses the difficult problem of finding an optimal neural architecture design for a given image classification task.
An evolutionary algorithm that constructs recurrent neural networks
P. J. Angeline, G. M. Saunders, and J. B. Pollack · 1994
Earlier work this paper cites.
Evolutionary algorithms for neural network design and training
J. Branke · 1995
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
K. O. Stanley and R. Miikkulainen · 2002
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
Earlier work this paper cites.
M. Lin, Q. Chen, and S. Yan · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Sequential model-based ensemble optimization
A. Lacoste, H. Larochelle, F. Laviolette, and M. Marchand · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Scalable bayesian optimization using deep neural networks
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. Patwary, M. Prabhat, and R. Adams · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, et al · 2015
Cited alongside, same era.
Designing neural network architectures using reinforcement learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 2016
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
R. Miikkulainen, J. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, A. Navruzyan, N. Duffy, and B. Hodjat · 2017
Later among the works it cites.
Deeparchitect: Automatically designing and training deep architectures
R. Negrinho and G. Gordon · 2017
Later among the works it cites.
Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, Q. Le, and A. Kurakin · 2017
Later among the works it cites.
Learning time-efficient deep architectures with budgeted super networks
T. Veniat and L. Denoyer · 2017
Later among the works it cites.
Genetic cnn
L. Xie and A. Yuille · 2017
Later among the works it cites.
Neural architecture search with reinforcement learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Convolutional neural fabrics
S. Saxena and J. Verbeek · 2016
Cited alongside, same era.
Smash: one-shot model architecture search through hypernetworks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
Cited alongside, same era.
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 · 2017
Cited alongside, same era.
B. Zoph and Q. V. Le · 2017
Later among the works it cites.
Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
Closest in time.
Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
Closest in time.
Practical network blocks design with Q-learning
Z. Zhong, J. Yan, and C.-L. Liu · 2018
Closest in time.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
Closest in time.