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The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods.
PC-DARTS: partial channel connections for memory-efficient differentiable architecture search
Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.J., Tian, Q., Xiong, H.: · 1907
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: · 2007
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Imagenet classification with deep convolutional neural networks
Alex, K., Ilya, S., E, H.G.: · 2012
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Deep learning
LeCun, Y., Bengio, Y., Hinton, G.E.: · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 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., Rabinovich, A.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
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Neural architecture search with reinforcement learning
Zoph, B., Le, Q.V.: · 2017
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In: ICML. (2017) 2902–2911
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, title=Large-scale evolution of image classifiers, Y.L., Tan, J., Le, Q.V., Kurakin, A · 2017
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Genetic cnn
Xie, L., Yuille, A.: · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: · 2017
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Accurate, large minibatch SGD: Training ImageNet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., He, K.: · 2017
Cited alongside, same era.
MobileNets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: · 2018
Cited alongside, same era.
SMASH: one-shot model architecture search through hypernetworks
Brock, A., Lim, T., Ritchie, J.M., Weston, N.: · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: · 2018
Cited alongside, same era.
NAS-FPN: learning scalable feature pyramid architecture for object detection
Ghiasi, G., Lin, T., Le, Q.V.: · 2019
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Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A., Fei-Fei, L.: · 2019
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Single-path nas: Designing hardware-efficient convnets in less than 4 hours
Stamoulis, D., Ding, R., Wang, D., Lymberopoulos, D., Priyantha, B., Liu, J., Marculescu, D.: · 2019
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Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search
Chu, X., Zhang, B., Xu, R., Li, J.: · 2019
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Searching for mobilenetv3
Howard, G., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q.V., Adam, H.: · 2019
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Progressive neural architecture search
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: · 2018
Cited alongside, same era.
Efficient architecture search by network transformation
Cai, H., Chen, T., Zhang, W., Yu, Y., Wang, J.: · 2018
Cited alongside, same era.
Neural architecture optimization
Luo, R., Tian, F., Qin, T., Chen, E., Liu, T.Y.: · 2018
Cited alongside, same era.
Hierarchical representations for efficient architecture search
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., Kavukcuoglu, K.: · 2018
Cited alongside, same era.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., Sun, J.: · 2018
Cited alongside, same era.
Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: · 2019
Cited alongside, same era.
DARTS: Differentiable architecture search
Liu, H., Simonyan, K., Yang, Y.: · 2019
Cited alongside, same era.
Mnasnet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Le, Q.V.: · 2019
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Understanding and robustifying differentiable architecture search
Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., Hutter, F.: · 2019
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Eliminating unfair advantages in differentiable architecture search
Chu, X., Zhou, T., Zhang, B., Li, J.: · 2019
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Xnas: Neural architecture search with expert advice
Nayman, N., Noy, A., Ridnik, T., Friedman, I., Jin, R., Zelnik, L.: · 2019
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BayesNAS: A Bayesian approach for neural architecture search
Zhou, H., Yang, M., Wang, J., Pan, W.: · 2019
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SNAS: Stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., Lin, L.: · 2019
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ProxylessNAS: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., Han, S.: · 2019
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