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Neural Architecture Search (NAS) explores a large space of architectural motifs -- a compute-intensive process that often involves ground-truth evaluation of each motif by instantiating it within a large network, and training and evaluating the network with thousands of domain-specific data samples.
Building a large annotated corpus of english: The penn treebank
Marcus, M. P., Marcinkiewicz, M. A., and Santorini, B · 1993
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Crafting papers on machine learning
Langley, P · 2000
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Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., and Raskar, R · 2016
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Accelerating neural architecture search using performance prediction
Baker, B., Gupta, O., Raskar, R., and Naik, N · 2017
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Learning curve prediction with bayesian neural networks
Klein, A., Falkner, S., Springenberg, J. T., and Hutter, F · 2017
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Large-scale evolution of image classifiers
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y. L., Tan, J., Le, Q. V., and Kurakin, A · 2017
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A genetic programming approach to designing convolutional neural network architectures
Suganuma, M., Shirakawa, S., and Nagao, T · 2017
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
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Understanding the role of steroids in typical and atypical brain development: Advantages of using a “brain in a dish” approach
Adhya, D., Annuario, E., Lancaster, M. A., Price, J., Baron-Cohen, S., and Srivastava, D. P · 2018
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Neural architecture search: A survey
Elsken, T., Metzen, J. H., and Hutter, F · 2018
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Neural architecture search with bayesian optimisation and optimal transport
Kandasamy, K., Neiswanger, W., Schneider, J., Poczos, B., and Xing, E. P · 2018
Cited alongside, same era.
Neural architecture optimization
Luo, R., Tian, F., Qin, T., Chen, E., and Liu, T.-Y · 2018
Cited alongside, same era.
Searching for activation functions
Ramachandran, P., Zoph, B., and Le, Q · 2018
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Learnable embedding space for efficient neural architecture compression
Cao, S., Wang, X., and Kitani, K. M · 2019
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Efficient multi-objective neural architecture search via lamarckian evolution
Elsken, T., Metzen, J. H., and Hutter, F · 2019
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Efficient forward architecture search
Hu, H., Langford, J., Caruana, R., Mukherjee, S., Horvitz, E., and Dey, D · 2019
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Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2019
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Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data, 2020
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Evolving deep neural networks
Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., Duffy, N., and Hodjat, B · 2018
Cited alongside, same era.
Efficient neural architecture search via parameters sharing
Pham, H., Guan, M., Zoph, B., Le, Q., and Dean, J · 2018
Cited alongside, same era.
Progressive neural architecture search
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., and Murphy, K
Cited in the paper.
Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y
Cited in the paper.
Such, F. P., Rawal, A., Lehman, J., Stanley, K., and Clune, J · 2020
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