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Automatic methods for Neural Architecture Search (NAS) have been shown to produce state-of-the-art network models.
A stochastic approximation method
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Optimization by simulated annealing
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Simulated annealing
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Optimal brain damage
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Second order derivatives for network pruning: Optimal brain surgeon
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Pruning backpropagation neural networks using modern stochastic optimisation techniques
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Simulated annealing overview
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Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
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Sparse online learning via truncated gradient
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80 million tiny images: A large data set for nonparametric object and scene recognition
A. Torralba, R. Fergus, and W. T. Freeman · 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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Reading digits in natural images with unsupervised feature learning
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Adam: A method for stochastic optimization
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Training deep neural networks on noisy labels with bootstrapping
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Learning both weights and connections for efficient neural networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 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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The freiburg groceries dataset
P. Jund, N. Abdo, A. Eitel, and W. Burgard · 2016
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Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2016
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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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Dropback: Continuous pruning during training
M. G. G. L. M. Lis · 2018
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Progressive neural architecture search
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy · 2018
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Neural architecture optimization
R. Luo, F. Tian, T. Qin, E. Chen, and T.-Y. Liu · 2018
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Efficient neural architecture search via parameter sharing
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Compression-aware training of deep networks
J. M. Alvarez and M. Salzmann · 2017
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Random erasing data augmentation
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
M. Zhu and S. Gupta · 2017
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H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, , and J. Dean · 2018
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
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You only search once: Single shot neural architecture search via direct sparse optimization
X. Zhang, Z. Huang, and N. Wang · 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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Probabilistic neural architecture search
F. P. Casale, J. Gordon, and N. Fusi · 2019
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Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
X. Chen, L. Xie, J. Wu, and Q. Tian · 2019
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Darts: Differentiable architecture search
L. Hanxiao, S. Karen, and Y. Yiming · 2019
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sharpdarts: Faster and more accurate differentiable architecture search
A. Hundt, V. Jain, and G. D. Hager · 2019
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K. A. Laube and A. Zell · 2019
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Darts+: Improved differentiable architecture search with early stopping
H. Liang, S. Zhang, J. Sun, X. He, W. Huang, K. Zhuang, and Z. Li · 2019
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Snas: Stochastic neural architecture search
S. Xie, H. Zheng, C. Liu, and L. Lin · 2019
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