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Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy.
Policy gradient methods for reinforcement learning with function approximation
R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour · 2000
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A perspective view and survey of meta-learning
R. Vilalta and Y. Drissi · 2002
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Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Conditional computation in neural networks for faster models
E. Bengio, P.-L. Bacon, J. Pineau, and D. Precup · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Dynamic deep neural networks: Optimizing accuracy-efficiency trade-offs by selective execution
L. Liu and J. Deng · 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. Le, and A. Kurakin · 2017
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Self-critical sequence training for image captioning
S. J. Rennie, E. Marcheret, Y. Mroueh, J. Ross, and V. Goel · 2017
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Genetic cnn
L. Xie and A. L. Yuille · 2017
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
G. Bender, P.-J. Kindermans, B. Zoph, V. Vasudevan, and Q. Le · 2018
Cited alongside, same era.
Dpp-net: Device-aware progressive search for pareto-optimal neural architectures
J.-D. Dong, A.-C. Cheng, D.-C. Juan, W. Wei, and M. Sun · 2018
Cited alongside, same era.
Multi-objective architecture search for cnns
T. Elsken, J. H. Metzen, and F. Hutter · 2018
Cited alongside, same era.
Munet: Macro unit-based convolutional neural network for mobile devices
D. Ha Kim, S. Hyun Lee, and B. Cheol Song · 2018
Cited alongside, same era.
Monas: Multi-objective neural architecture search using reinforcement learning
Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Igcv3: Interleaved low-rank group convolutions for efficient deep neural networks
K. Sun, M. Li, D. Liu, and J. Wang · 2018
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Mnasnet: Platform-aware neural architecture search for mobile
M. Tan, B. Chen, R. Pang, V. Vasudevan, and Q. V. Le · 2018
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Hydranets: Specialized dynamic architectures for efficient inference
R. Teja Mullapudi, W. R. Mark, N. Shazeer, and K. Fatahalian · 2018
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C.-H. Hsu, S.-H. Chang, D.-C. Juan, J.-Y. Pan, Y.-T. Chen, W. Wei, and S.-C. Chang · 2018
Cited alongside, same era.
Stochastic downsampling for cost-adjustable inference and improved regularization in convolutional networks
J. Kuen, X. Kong, Z. Lin, G. Wang, J. Yin, S. See, and Y.-P. Tan · 2018
Cited alongside, same era.
Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
Cited alongside, same era.
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
L. McInnes, J. Healy, and J. Melville · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection
L. McInnes, J. Healy, N. Saul, and L. Grossberger · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
Cited alongside, same era.
A. Veit and S. Belongie · 2018
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Learning time/memory-efficient deep architectures with budgeted super networks
T. Véniat and L. Denoyer · 2018
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Transfer learning with neural automl
C. Wong, N. Houlsby, Y. Lu, and A. Gesmundo · 2018
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Blockdrop: Dynamic inference paths in residual networks
Z. Wu, T. Nagarajan, A. Kumar, S. Rennie, L. S. Davis, K. Grauman, and R. Feris · 2018
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Netadapt: Platform-aware neural network adaptation for mobile applications
T.-J. Yang, A. Howard, B. Chen, X. Zhang, A. Go, M. Sandler, V. Sze, and H. Adam · 2018
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Resource-efficient neural architect
Y. Zhou, S. Ebrahimi, S. Ö. Arık, H. Yu, H. Liu, and G. Diamos · 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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