Neural Architecture Search without Training

Simple statistical gradient-following algorithms for connectionist reinforcement learning

Williams, R. J · 1992

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Learning multiple layers of features from tiny images

Krizhevsky, A · 2009

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ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., and Hinton, G · 2012

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ImageNet large scale visual recognition challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015

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Very deep convolutional networks for large-scale image recognition

Simonyan, K. and Zisserman, 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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Rethinking the inception architecture for computer vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016

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A downsampled variant of imagenet as an alternative to the cifar datasets

Original

Chrabaszcz, P., Loshchilov, I., and Hutter, F · 2017

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Peephole: Predicting network performance before training

Original

Deng, B., Yan, J., and Lin, D · 2017

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Densely connected convolutional networks

Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017

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Neural architecture search with reinforcement learning

Zoph, B. and Le, Q. V · 2017

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SMASH: One-shot model architecture search through hypernetworks

Brock, A., Lim, T., Ritchie, J., and Weston, N · 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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Neural tangent kernel: Convergence and generalization in neural networks

Jacot, A., Gabriel, F., and Hongler, C · 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 · 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.

Efficient neural architecture search via parameter sharing

Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., and Dean, J · 2018

Cited alongside, same era.

Learning transferable architectures for scalable image recognition

Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018

Cited alongside, same era.

ProxylessNAS: Direct neural architecture search on target task and hardware

Cai, H., Zhu, L., and Han, S · 2019

Cited alongside, same era.

MnasNet: Platform-aware neural architecture search for mobile

Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V · 2019

Later among the works it cites.

A survey on neural architecture search

Original

Wistuba, M., Rawat, A., and Pedapati, T · 2019

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FBNet: Hardware-aware efficient convnet design via differentiable neural architecture search

Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., Jia, Y., and Keutzer, K · 2019

Later among the works it cites.

Snas: stochastic neural architecture search

Xie, S., Zheng, H., Liu, C., and Lin, L · 2019

Later among the works it cites.

Nas-bench-101: Towards reproducible neural architecture search

Ying, C., Klein, A., Real, E., Christiansen, E., Murphy, K., and Hutter, F · 2019

Later among the works it cites.