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Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation.
Evaluating the search phase of neural architecture search
Sciuto, C., Yu, K., Jaggi, M., Musat, C., and Salzmann, M · 1902
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Correlated and uncorrelated fitness landscapes and how to tell the difference
Weinberger, E · 1990
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Evolutionary algorithms, fitness landscapes and search
Jones, T. et al · 1995
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Landscapes and their correlation functions
Stadler, P. F · 1996
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Algorithms for hyper-parameter optimization
Bergstra, J. S., Bardenet, R., Bengio, Y., and Kégl, B · 2011
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Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K · 2011
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Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G · 2012
Earlier work this paper cites.
Towards an empirical foundation for assessing bayesian optimization of hyperparameters
Eggensperger, K., Feurer, M., Hutter, F., Bergstra, J., Snoek, J., Hoos, H., and Leyton-Brown, K · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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Scalable Bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, and Adams, R · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N · 2016
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Bayesian optimization with robust bayesian neural networks
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F · 2016
Multi-objective architecture search for cnns
Elsken, T., Metzen, J. H., and Hutter, F · 2018
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Bohb: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F · 2018
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Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G · 2018
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Huang, Y., Cheng, Y., Chen, D., Lee, H., Ngiam, J., Le, Q. V., and Chen, Z · 2018
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2018
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Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2016
Cited alongside, same era.
Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., and Raskar, R · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K. Q., and van der Maaten, L · 2017
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
Cited alongside, same era.
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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Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
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Neural architecture search: A survey
Elsken, T., Metzen, J. H., and Hutter, F · 2019
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Tabular benchmarks for joint architecture and hyperparameter optimization
Klein, A. and Hutter, F · 2019
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Random Search and Reproducibility for Neural Architecture Search
Li, L. and Talwalkar, A · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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So, D. R., Liang, C., and Le, Q. V · 2019
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Enumerating unique computational graphs via an iterative graph invariant
Ying, C · 2019
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