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We introduce a new function-preserving transformation for efficient neural architecture search.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Earlier work this paper cites.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Domhan, T., Springenberg, J. T., and Hutter, F · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Earlier work this paper cites.
Improved semantic representations from tree-structured long short-term memory networks
Tai, K. S., Socher, R., and Manning, C. D · 2015
Earlier work this paper cites.
Net2net: Accelerating learning via knowledge transfer
Chen, T., Goodfellow, I., and Shlens, J · 2016
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Xception: Deep learning with depthwise separable convolutions
Chollet, F · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Sgdr: stochastic gradient descent with restarts
Loshchilov, I. and Hutter, F · 2016
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Towards automatically-tuned neural networks
Mendoza, H., Klein, A., Feurer, M., Springenberg, J. T., and Hutter, F · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Deeparchitect: Automatically designing and training deep architectures
Negrinho, R. and Gordon, G · 2017
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Large-scale evolution of image classifiers
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y. L., Le, Q., and Kurakin, A · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A · 2017
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., and Sun, J · 2017
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Residual networks behave like ensembles of relatively shallow networks
Veit, A., Wilber, M. J., and Belongie, S · 2016
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Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., and Raskar, R · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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Gastaldi, X · 2017
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Deep pyramidal residual networks
Han, D., Kim, J., and Kim, J · 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., and Adam, H · 2017
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Zhong, Z., Yan, J., and Liu, C.-L · 2017
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
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Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2017
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N2n learning: Network to network compression via policy gradient reinforcement learning
Ashok, A., Rhinehart, N., Beainy, F., and Kitani, K. M · 2018
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Efficient architecture search by network transformation
Cai, H., Chen, T., Zhang, W., Yu, Y., and Wang, J · 2018
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Hierarchical representations for efficient architecture search
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., and Kavukcuoglu, K · 2018
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Efficient neural architecture search via parameter sharing
Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., and Dean, J · 2018
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Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2018
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