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Neural architecture search (NAS) methods rely on a search strategy for deciding which architectures to evaluate next and a performance estimation strategy for assessing their performance (e.g., using full evaluations, multi-fidelity evaluations, or the one-shot model).
David R. So, Chen Liang, and Quoc V. Le · 1901
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Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 1902
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Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search
A. Zela, J. Siems, and F. Hutter · 1902
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Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
R. Storn and P. Kenneth · 1997
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An algorithm for determining neural network architecture using differential evolution
Md. Zakirul Alam Bhuiyan · 2009
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A hypercube-based encoding for evolving large-scale neural networks
K. Stanley, D. D’Ambrosio, and J. Gauci · 2009
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Topology optimization for artificial neural networks using differential evolution
Nicole L. Mineu, Teresa B. Ludermir, and Leandro M. Almeida · 2010
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Algorithms for hyper-parameter optimization
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 2011
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network
H. Dhahri, A. Alimi, and A. Abraham · 2012
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Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2017
Cited alongside, same era.
Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. Suematsu, Q. Le, and A. Kurakin · 2017
Cited alongside, same era.
Efficient multi-objective neural architecture search via lamarckian evolution
T. Elsken, J. Metzen, and F. Hutter · 2018
Cited alongside, same era.
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
S. Falkner, A. Klein, and F. Hutter · 2018
Cited alongside, same era.
Towards reproducible neural architecture and hyperparameter search
A. Klein, E. Christiansen K. Murphy, and F. Hutter · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2018
Later among the works it cites.
Tabular benchmarks for joint architecture and hyperparameter optimization
A. Klein and F. Hutter · 2019
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Best Practices for Scientific Research on Neural Architecture Search
Marius Lindauer and Frank Hutter · 2019
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Designing neural networks through neuroevolution
K. Stanley, J. Clune, J. Lehman, and R. Miikkulainen · 2019
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NAS-bench-101: Towards reproducible neural architecture search
C. Ying, A. Klein, E. Christiansen, E. Real, K. Murphy, and F. Hutter · 2019
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Hyperband: A novel bandit-based approach to hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2018
Cited alongside, same era.
DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
Cited alongside, same era.
Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. Le · 2018
Cited alongside, same era.
Neural architecture search: A survey
T. Elsken, J. Metzen, and F. Hutter
Cited in the paper.
Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter
Cited in the paper.
Neural network topology and weight optimization through neuro differential evolution
K. Mason, J. Duggan, and E. Howley
Cited in the paper.
Evolving feedforward artificial neural networks using a two-stage approach
L. Zhang, H. Li, and X. Kong · 2019
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Differential evolution for neural networks optimization
M. Baioletti, G. Di Bari, A. Milani, and Valentina Poggioni · 2020
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Nas-bench-201: Extending the scope of reproducible neural architecture search
X. Dong and Y. Yang · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake Vand erPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1. 0 Contributors · 2020
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