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Efficient evaluation of a network architecture drawn from a large search space remains a key challenge in Neural Architecture Search (NAS).
Evaluating the search phase of neural architecture search
Yu, K., Sciuto, C., Jaggi, M., Musat, C., and Salzmann, M · 1902
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Alphax: exploring neural architectures with deep neural networks and monte carlo tree search
Wang, L., Zhao, Y., Jinnai, Y., Tian, Y., and Fonseca, R · 1903
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Network slimming by slimmable networks: Towards one-shot architecture search for channel numbers
Yu, J. and Huang, T. S · 1903
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Single path one-shot neural architecture search with uniform sampling
Guo, Z., Zhang, X., Mu, H., Heng, W., Liu, Z., Wei, Y., and Sun, J · 1904
Earlier work this paper cites.
Howard, A., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V., and Adam, H · 1905
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q. V · 1905
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Sample-efficient neural architecture search by learning action space
Wang, L., Xie, S., Li, T., Fonseca, R., and Tian, Y · 1906
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Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search
Chu, X., Zhang, B., Xu, R., and Li, J · 1907
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Once for all: Train one network and specialize it for efficient deployment
Cai, H., Gan, C., Wang, T., Zhang, Z., and Han, S · 1908
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Balanced one-shot neural architecture optimization
Luo, R., Qin, T., and Chen, E · 1909
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Fair DARTS: eliminating unfair advantages in differentiable architecture search
Chu, X., Zhou, T., Zhang, B., and Li, J · 1911
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Progressive DARTS: bridging the optimization gap for NAS in the wild
Chen, X., Xie, L., Wu, J., and Tian, Q · 1912
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A new measure of rank correlation
Kendall., M. G · 1938
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The Penn Treebank: Annotating predicate argument structure
Marcus, M., Kim, G., Marcinkiewicz, M. A., MacIntyre, R., Bies, A., Ferguson, M., Katz, K., and Schasberger, B · 1994
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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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Algorithms for hyper-parameter optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B · 2012
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., and Raskar, R · 2017
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Improved regularization of convolutional neural networks with cutout
Devries, T. and Taylor, G. W · 2017
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Understanding and simplifying one-shot architecture search
Bender, G., Kindermans, P.-J., Zoph, B., Vasudevan, V., and Le, Q · 2018
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On layer-level control of DNN training and its impact on generalization
Carbonnelle, S. and Vleeschouwer, C. D · 2018
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Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mané, D., Vasudevan, V., and Le, Q. V · 2018
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BOHB: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F · 2018
Cited alongside, same era.
Probgan: Towards probabilistic gan with theoretical guarantees
He, H., Wang, H., Lee, G.-H., and Tian, Y · 2019
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Xnas: Neural architecture search with expert advice
Nayman, N., Noy, A., Ridnik, T., Friedman, I., Jin, R., and Zelnik, L · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., and Le, Q. V · 2019
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NAS-bench-101: Towards reproducible neural architecture search
Ying, C., Klein, A., Christiansen, E., Real, E., Murphy, K., and Hutter, F · 2019
Later among the works it cites.
BayesNAS: A Bayesian approach for neural architecture search
Zhou, H., Yang, M., Wang, J., and Pan, W · 2019
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MGAN: Training generative adversarial nets with multiple generators
Hoang, Q., Nguyen, T. D., Le, T., and Phung, D · 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
Cited alongside, same era.
Neural architecture optimization
Luo, R., Tian, F., Qin, T., Chen, E., and Liu, T.-Y · 2018
Cited alongside, same era.
Shufflenet V2: practical guidelines for efficient CNN architecture design
Ma, N., Zhang, X., Zheng, H., and Sun, J · 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.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q · 2018
Cited alongside, same era.
Later among the works it cites.
Nas-bench-201: Extending the scope of reproducible neural architecture search
Dong, X. and Yang, Y · 2020
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Angle-based search space shrinking for neural architecture search
Hu, Y., Liang, Y., Guo, Z., Wan, R., Zhang, X., Wei, Y., Gu, . Q., and Sun, J · 2020
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{CNAS}: Channel-level neural architecture search, 2020
Lim, H., Kim, M.-S., and Xiong, J · 2020
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Atomnas: Fine-grained end-to-end neural architecture search
Mei, J., Li, Y., Lian, X., Jin, X., Yang, L., Yuille, A., and Yang, J · 2020
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Designing network design spaces
Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., and Dollar, P · 2020
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Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions
Wan, A., Dai, X., Zhang, P., He, Z., Tian, Y., Xie, S., Wu, B., Yu, M., Xu, T., Chen, K., Vajda, P., and Gonzalez, J. E · 2020
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Mergenas: Merge operations into one for differentiable architecture search
Wang, X., Xue, C., Yan, J., Yang, X., Hu, Y., and Sun, K · 2020
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{PC}-{darts}: Partial channel connections for memory-efficient architecture search
Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.-J., Tian, Q., and Xiong, H · 2020
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Evaluating the search phase of neural architecture search
Yu, K., Sciuto, C., Jaggi, M., Musat, C., and Salzmann, M · 2020
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One-shot neural architecture search via novelty driven sampling
Zhang, M., Li, H., Pan, S., Liu, T., and Su, S · 2020
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An exponential learning rate schedule for deep learning
Zhiyuan Li, S. A · 2020
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