Fetching the paper…
Reading the bibliography…
Deep learning has shown promising results on many machine learning tasks but DL models are often complex networks with large number of neurons and layers, and recently, complex layer structures known as building blocks.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
J. Bergstra, D. Yamins, and D. Cox · 2013
Earlier work this paper cites.
Deep learning using linear support vector machines
Y. Tang · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Optimizing deep learning hyper-parameters through an evolutionary algorithm
S. R. Young, D. C. Rose, T. P. Karnowski, S.-H. Lim, and R. M. Patton · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2016
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
Reinforcement learning for architecture search by network transformation
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang · 2017
Later among the works it cites.
X. Gastaldi · 2017
Later among the works it cites.
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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2016
Cited alongside, same era.
S. Zagoruyko and N. Komodakis · 2016
Cited alongside, same era.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
Cited alongside, same era.
https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge
Facial expression recognition challenge
Cited in the paper.
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, Q. Le, and A. Kurakin · 2017
Later among the works it cites.
A genetic programming approach to designing convolutional neural network architectures
M. Suganuma, S. Shirakawa, and T. Nagao · 2017
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
Later among the works it cites.
Practical network blocks design with q-learning
Z. Zhong, J. Yan, and C.-L. Liu · 2017
Later among the works it cites.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
Later among the works it cites.