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DARTS is a popular algorithm for neural architecture search (NAS).
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. E · 2015
Earlier work this paper cites.
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., et al · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Earlier work this paper cites.
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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Genetic CNN
Xie, L. and Yuille, A. L · 2017
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
Earlier work this paper cites.
Efficient architecture search by network transformation
Cai, H., Chen, T., Zhang, W., Yu, Y., and Wang, J · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G · 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. T., and Sun, J · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Pham, H., Guan, M., Zoph, B., Le, Q. V., and Dean, J · 2018
Cited alongside, same era.
SNAS: Stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., and Lin, L · 2018
Cited alongside, same era.
Darts+: Improved differentiable architecture search with early stopping
Liang, H., Zhang, S., Sun, J., He, X., Huang, W., Zhuang, K., and Li, Z · 2019
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Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, 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-Manor, L · 2019
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Asap: Architecture search, anneal and prune
Noy, A., Nayman, N., Ridnik, T., Zamir, N., Doveh, S., Friedman, I., Giryes, R., and Zelnik-Manor, L · 2019
Closest in time.
Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2019
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., and Sun, 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.
Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Chen, X., Xie, L., Wu, J., and Tian, Q · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
Cited alongside, same era.
Searching for a robust neural architecture in four gpu hours
Dong, X. and Yang, Y · 2019
Cited alongside, same era.
Evaluating the search phase of neural architecture search
Sciuto, C., Yu, K., Jaggi, M., Musat, C., and Salzmann, M · 2019
Closest in time.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q. V · 2019
Closest in time.
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
Closest in time.
Exploring randomly wired neural networks for image recognition
Xie, S., Kirillov, A., Girshick, R., and He, K · 2019
Closest in time.
Bayesnas: A bayesian approach for neural architecture search
Zhou, H., Yang, M., Wang, J., and Pan, W · 2019
Closest in time.
Pc-darts: Partial channel connections for memory-efficient differentiable architecture search
Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G. J., Tian, Q., and Xiong, H · 2020
Closest in time.
Efficient neural architecture search via proximal iterations
Yao, Q., Xu, J., Tu, W. W., and Zhu, Z · 2020
Closest in time.
Understanding and robustifying differentiable architecture search
Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., and Hutter, F · 2020
Closest in time.