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Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performances in applications such as image classification and language modeling.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R.J.: · 1992
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G.: · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H.H., Leyton-Brown, K.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Deep speech: Scaling up end-to-end speech recognition
Hannun, A., Case, C., Casper, J., Catanzaro, B., Diamos, G., Elsen, E., Prenger, R., Satheesh, S., Sengupta, S., Coates, A., et al.: · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., Le, Q.V.: · 2014
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
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Neural architecture search with reinforcement learning
Zoph, B., Le, Q.V.: · 2016
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Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., Raskar, R.: · 2016
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Xception: Deep learning with depthwise separable convolutions
Chollet, F.: · 2016
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Ba, J.L., Kiros, J.R., Hinton, G.E.: · 2016
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Sgdr: stochastic gradient descent with restarts
Loshchilov, I., Hutter, F.: · 2016
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Deeparchitect: Automatically designing and training deep architectures
Negrinho, R., Gordon, G.: · 2017
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Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: · 2017
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Practical network blocks design with q-learning
Zhong, Z., Yan, J., Liu, C.L.: · 2017
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Condensenet: An efficient densenet using learned group convolutions
Huang, G., Liu, S., van der Maaten, L., Weinberger, K.Q.: · 2017
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Nemo: Neuro-evolution with multiobjective optimization of deep neural network for speed and accuracy
Kim, Y.H., Reddy, B., Yun, S., Seo, C.: · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: · 2017
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Efficient architecture search by network transformation
Cai, H., Chen, T., Zhang, W., Yu, Y., Wang, J.: · 2017
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Smash: one-shot model architecture search through hypernetworks
Brock, A., Lim, T., Ritchie, J.M., Weston, N.: · 2017
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Large-scale evolution of image classifiers
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y.L., Le, Q., Kurakin, A.: · 2017
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Genetic cnn
Xie, L., Yuille, A.: · 2017
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Hierarchical representations for efficient architecture search
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., Kavukcuoglu, K.: · 2017
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Progressive neural architecture search
Liu, C., Zoph, B., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., Sun, J.: · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: · 2017
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Efficient neural architecture search via parameter sharing
Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: · 2018
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Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: · 2018
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Accelerating neural architecture search using performance prediction
Baker, B., Gupta, O., Raskar, R., Naik, N.: · 2018
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